In this exclusive interview, IoT Business News sits down with Steven Baker, Chief Product Officer at KORE*, to discuss the company’s groundbreaking achievements, recent technological advancements, and strategic initiatives in the rapidly evolving Internet of Things (IoT) landscape.

From the acquisition of Twilio’s IoT connectivity business to the development of cutting-edge eUICC technology, Steven provides insights into how KORE is positioning itself as a leader in the market. He also shares success stories, highlights partnerships, and outlines the company’s future plans, offering a comprehensive overview of KORE’s vision and impact in the IoT space.

IoT Business News: Can you elaborate on KORE’s most significant achievements in the IoT space over the past year and how these have positioned the company in the market?

Steven Baker: The acquisition of Twilio’s IoT connectivity business unit has brought KORE an unparalleled combination of carrier IMSI and eUICC product capability. It has also positioned KORE with a highly scalable route to market with a self-service, developer-centric model enabling KORE to capitalize on the OEM drive for built-in connectivity during the manufacturing process.

Over the last year KORE has also launched the Pre-configured Solutions (PCS) business unit focused on delivering high-value IoT enablement services incorporating connectivity, hardware, and managed services into pre-configured bundles that reduce the complexity of deploying IoT applications.

KORE has also reached over 19 million IoT subscriptions in service making KORE one of the largest IoT MVNOs in the world.

What are the latest technological advancements that KORE has integrated into its IoT solutions? How are these innovations enhancing your offerings compared to your competitors?

One of the most recent technological advancements KORE has released is an eUICC technology called the Local Profile Management Applet. The KORE LPM Applet enables a host CPU to manage previously downloaded eSIM profiles locally using AT commands. This capability can be used to implement device-initiated fallback, device-initiated failover, resiliency, and similar advanced capabilities. The LPM applet combines the simplicity and reliability of M2M orchestration with the flexibility and power of local profile management, in a manner that’s forward-compatible with the SGP32 IoT standard. The applet interface is based on the SGP22 ES10 standard ensuring developers who use it get to keep their investment as they eventually migrate to SGP22 and SGP32 solutions.

Could you share a recent customer success story that highlights the effectiveness of KORE’s IoT solutions in transforming their business operations?

A key element of KORE’s ability to positively impact our customer’s business operations is through our multi-country, managed service and logistics solutions. KORE has a long history serving Healthcare OEMs and DMEs with critical healthcare logistics solutions. Recently, two KORE customers in this space experienced a 40% reduction in annualized new inventory spend by leveraging KORE’s reverse logistics, sanatization, and redeployment of pre-used equipment including custom device configuration enabling a single device to support multiple business lines.

With increasing concerns around IoT security, what steps has KORE taken to enhance the security features of its solutions?

KORE’s eSIM technology includes support for IoT SAFE. IoT SAFE establishes the SIM as the ‘Root of Trust’ to enable chip-to-cloud security. This technology can be configured during SIM manufacturing making it both scalable and simple to leverage using standard (D)TLS standards. It obviates the need for post manufacturing device provisioning which can be difficult to both provision and maintain when requiring periodic security updates over time.

How do partnerships shape KORE’s strategy in expanding its IoT solutions? Are there any recent or upcoming collaborations that we should be aware of?

KORE’s partner portfolio spans 30+ MNOs worldwide as well as many professional and managed service providers. These partnerships enable KORE to pull together nearly any combination of connectivity, hardware, and service to solve IoT challenges. KORE also has strong relationships with multiple cloud providers including most recently with Google. Recently, KORE also added partners with multiple third-party service providers to augment our manual and professional service portfolio to enable our vertically focused pre-configured solutions.

What markets does KORE plan to target in the near future? Are there new industries or regions where you see significant growth opportunities for your IoT solutions?

From a regional perspective, KORE has traditionally had a significant presence in North America and has been growing in the UK and EU with localized sales, logistics, and support teams. KORE’s carrier partner ecosystem is expanding in 2024 enabling KORE to focus more on the APAC region, and more specifically China.

From a vertical market perspective, KORE’s current PCS solutions target 3 vertical areas including enterprise Fixed Wireless (FW), Connected Health, and Fleet Management. These solutions are deployed throughout North America and are expanding into the UK, Europe, and Latin America in the coming months. KORE is also currently trialing an Industrial pre-configured solution.

How is KORE leveraging its IoT technology to promote sustainability within its operations and among its clients? Are there specific initiatives or projects that exemplify this approach?

KORE launched an initiative to reduce waste and support sustainability by reducing the size of card bodies in its SIM shipments, which ties into “IoT for Good” – a key initiative here at KORE that leverages innovations in IoT such as SIM and connectivity to enable us to live greener and longer, all while making informed, intelligent use of our global resources. Since the commercial launch of SIM cards three decades ago, approximately 4.5 billion SIM cards are sold and shipped each year industry-wide, accounting for more than 560,000 tons of carbon dioxide and 18,000+ tons of plastic waste annually. While the SIM card has reduced in size over the last three decades, the packaging the card body that holds the SIM has not. The KORE initiative reduces the card body by 50% and, relating to SIM cards, is expected to:

• Reduce shipping costs by 50% due to the weight reduction
• Reduce KORE’s carbon footprint by 16%
• Aid customers in reducing plastic waste by 50%

Looking ahead, what are the next big steps for KORE in terms of product development and market strategies? Are there any upcoming innovations or technologies that you are particularly excited about introducing to the market?

KORE is evolving our eSIM technology to incorporate the SGP.32 (IoT) standard and increasing our coverage footprint with in-country coverage in the APAC region. OEMs worldwide will be seeking ways to leverage iSIM technology to open up new service potential and simplify logistics and provisioning at scale.

KORE also continues to evolve our AI modeling with new initiatives around it for managing IoT real-time operations and logistics analysis and monitoring as well as custom AI solutions for individual use cases requiring intelligent automation at scale.

The post Innovating IoT: An Exclusive Interview with KORE’s Chief Product Officer Steven Baker appeared first on IoT Business News.

Introduction

In the expansive realm of the Internet of Things (IoT), seamless and efficient communication is paramount. As industries evolve to become smarter and more interconnected, the role of IoT protocols becomes increasingly critical. MQTT (Message Queuing Telemetry Transport) and OPC UA (Open Platform Communications Unified Architecture) are two of the foremost protocols spearheading this advancement in industrial and automation sectors. Their robustness, flexibility, and security features make them indispensable in today’s IoT ecosystem. This article series will explore each protocol in depth, illustrating their importance and functionalities, and why they are considered foundational components of IoT communications.

Understanding MQTT

History and Origin

Developed in 1999 by Andy Stanford-Clark of IBM and Arlen Nipper of Cirrus Link (formerly Arcom), MQTT was originally created to support connections with remote oil pipelines over satellite networks, which required a highly efficient messaging system. The protocol’s design was specifically tailored to conserve bandwidth and ensure reliable message delivery in environments with limited connectivity, making it an ideal choice for various telemetry applications.

Core Concepts and Architecture

MQTT is fundamentally built on a publish/subscribe messaging pattern. This architecture is facilitated by a central broker that manages all message transmissions. Clients, which can be either publishers or subscribers, connect to the broker. Publishers send messages to the broker, tagging them with a topic, and subscribers receive messages by subscribing to these topics.

Broker

The broker is the heart of MQTT’s architecture. It is responsible for receiving all messages, filtering them, determining who is interested, and then publishing those messages to subscribed clients. This decoupling of publishers and subscribers allows the network to efficiently scale to a large number of distributed systems.

Publisher/Subscriber Model

The publisher/subscriber model eliminates the need for direct connections between devices, which enhances scalability and reduces system complexity. This model is particularly effective in IoT scenarios where many devices need to send data to multiple consumers who may have different data requirements.

Topics and Message Structure

• Topics: In MQTT, topics are used to route messages from publishers to subscribers. The topic namespace is hierarchical and resembles a filesystem path structure, which allows for expressive and flexible topic filtration.
• Message Structure: MQTT messages are composed of three parts:
• Fixed header: Contains essential metadata about the message, such as the message type and the QoS level.
• Variable header: Optional and context-specific, includes elements like topic name and packet identifier.
• Payload: The actual data being sent, which can be up to 256 MB in size.

Key Features

• Quality of Service (QoS): MQTT offers three levels of QoS to cater to different delivery guarantees:
• QoS 0 (At most once): Best-effort delivery without confirmation.
• QoS 1 (At least once): Ensures the message arrives at least once.
• QoS 2 (Exactly once): Guarantees message delivery exactly once.
• Last Will and Testament (LWT): This feature is crucial for detecting and responding to client disconnections from the broker. A client can specify a will message that the broker will send to interested parties if it disconnects ungracefully.

Advantages and Use Cases

MQTT’s lightweight packet structure and efficient distribution mechanism make it highly suitable for environments with limited network bandwidth. Its use cases are broad, covering:

• IoT applications where devices periodically send data like temperature or status updates.
• Implementations requiring real-time updates and alerts, such as security systems and vehicle tracking.

Ideal Scenarios for MQTT Usage

• Telemetry: Data collection from field devices such as in agriculture for monitoring soil moisture.
• Home Automation: Controlling lights, locks, and other home systems remotely.

Case Studies Highlighting MQTT Implementation

Industries like manufacturing and healthcare have leveraged MQTT to enhance operational efficiency and patient care. For example, MQTT has been used to monitor manufacturing equipment to predict maintenance needs and in healthcare settings to manage asset tracking of medical equipment.

Demystifying OPC UA

Historical Context and Development

OPC UA was developed by the OPC Foundation as a successor to the original OPC (OLE for Process Control) standards, which were tied to Windows operating system platforms and dependent on Microsoft’s COM/DCOM technologies. Recognizing the need for a platform-independent, more secure, and scalable architecture, the OPC Foundation introduced OPC UA in 2006. This new protocol was designed to support complex data types and offer a secure and reliable communication framework suitable for the demands of industrial automation.

Core Concepts and Architecture

OPC UA is more than just a protocol; it is a comprehensive framework for data exchange and a machine-to-machine communication paradigm in industrial automation. Unlike MQTT, which primarily focuses on data transport, OPC UA also emphasizes data modeling, which is crucial for representing the semantics and metadata of information.

Server/Client Model

• Server: In OPC UA, the server provides data to clients and offers interfaces for clients to interact with it. It handles all aspects of the data management, security, and communications.
• Client: The client consumes the data provided by the server and can also send commands or write data back to the server, depending on the permissions set by the system.

Information Modeling in OPC UA

One of OPC UA’s standout features is its robust information modeling capabilities, which allow it to not only transport data but also describe what the data represents. This is essential in industrial applications where understanding the context of data is as important as the data itself.

• Nodes and References: At its core, OPC UA represents data as nodes, which can be objects, variables, or methods. These nodes are interconnected by references, which can represent hierarchical relationships or data flows.
• Address Space: OPC UA utilizes a structured address space that contains nodes representing all data and services accessible from the server. This approach allows clients to discover data and functionalities dynamically and interact with them in a standardized way.

Key Features

• Platform Independence: Unlike its predecessors, OPC UA is platform-agnostic, capable of running on any system from embedded microcontrollers to cloud-based servers.
• Built-in Security Mechanisms: OPC UA offers comprehensive security features, including encryption, authentication, and user control, making it suitable for critical industrial applications.
• Data Encryption and Authentication: These features ensure that data is not only secure during transmission but also that the entities exchanging information are verified.

Advantages and Use Cases

OPC UA’s sophisticated information modeling and security features make it ideal for complex industrial automation tasks where multiple systems need to interact seamlessly and securely.

• Industry 4.0 and Smart Factories: OPC UA is crucial in the context of Industry 4.0, where it facilitates interoperability among various devices and systems within smart factories.
• Energy Management: OPC UA is used in energy management systems for efficient monitoring and control of electrical systems across grids.

Real-world Examples of OPC UA in Action

Several industrial automation giants have adopted OPC UA to enhance connectivity and automation in their processes. For instance, major automotive manufacturers use OPC UA for device interoperability and to streamline their production lines, enhancing real-time data exchange and system interoperability.

MQTT vs. OPC UA

Below is a detailed comparison of MQTT and OPC UA presented in a tabular format, highlighting their primary characteristics, advantages, and typical use cases to facilitate a clear understanding of when and why each might be preferred in different IoT environments.

Comparative Table: MQTT vs. OPC UA

This table summarizes the distinct aspects and capabilities of MQTT and OPC UA, helping to clarify the scenarios in which each protocol excels. MQTT is generally better suited for less complex, high-volume, and resource-constrained environments. In contrast, OPC UA is tailored for industrial applications where detailed data modeling, robust security, and reliable communications are paramount.

When selecting a protocol, consider the specific demands of your application, including data complexity, required security level, network stability, and scalability needs. This comparison should serve as a guide to making an informed decision that aligns with the technical requirements and goals of your IoT or IIoT deployment.

The post Exploring MQTT & OPC UA: The Backbone of IoT Communication appeared first on IoT Business News.

The transition to sustainable energy systems is one of the most significant challenges of our time, requiring innovative technologies to optimize efficiency and integration at the enterprise level across numerous sectors. Among the most transformative technologies in this regard is the Internet of Things (IoT), which is continuously reshaping the landscape of energy management.

By enabling more efficient monitoring, management, and optimization of energy, IoT technologies are pivotal in advancing the energy transition across multiple fronts by enabling more efficient monitoring, management, and optimization of energy.

Secure and Efficient Energy Systems with IoT

Secure connectivity is essential in today’s energy systems, providing the backbone for reliable and resilient energy management. IoT technology augments this foundation by facilitating safe, real-time monitoring and data collection from various distributed energy resources. This secure data transit is crucial for protecting systems against cyber threats while ensuring that energy operations are efficient and adaptable to real-time conditions.

Furthermore, IoT’s impact extends significantly to optimizing renewable energy sources such as solar power. Through IoT-enabled devices, energy providers can perform real-time monitoring and predictive maintenance, which are vital for minimizing downtime and improving grid stability. These systems utilize advanced analytics to forecast energy output and adjust grid operations dynamically, enhancing the integration of renewable resources like solar and wind. Despite the cybersecurity challenges and the complexity of achieving seamless interoperability among diverse technologies, the robust capabilities of IoT provide a solid foundation for overcoming these hurdles and maximizing the efficiency of solar energy solutions.

Empowering Consumer Engagement and Decentralized Systems

IoT-driven advancements in the energy space extend beyond industry-level operations; they also directly impact everyday consumers. These technologies revolutionize how energy providers operate and transform how consumers conceptualize, interact with, and manage their energy usage.

By delivering detailed consumption data directly to consumers, IoT devices empower individuals with the knowledge to make informed decisions about their energy use through real-time monitoring, personalized insights, and demand response capabilities. These factors drive behavioral changes and foster greater energy efficiency and sustainability at the consumer level.

Additionally, IoT plays a crucial role in facilitating the rise of decentralized energy systems. These systems allow for more localized management of energy resources, reducing dependencies on centralized grids and enhancing community resilience to energy disruptions. IoT enables efficient management and coordination of distributed energy resources (DERs), such as residential solar panels and community battery storage, providing a seamless flow of energy and real-time adjustments based on local consumption and production patterns.

Navigating Challenges and Seizing Opportunities

While IoT introduces transformative potential for the energy sector, it also brings challenges, particularly in cybersecurity and data privacy. These concerns are paramount as energy systems become increasingly interconnected and reliant on digital infrastructures. However, the strategic implementation of IoT can address these challenges by enhancing system security and reliability through continuous monitoring and advanced cybersecurity protocols.

The opportunities for growth and improvement are vast. IoT technologies not only enhance operational efficiencies but also support crucial sustainability goals by more effectively integrating renewable energy sources into the grid. Moreover, they enable predictive maintenance capabilities that reduce downtime and operational costs, all while improving the reliability and lifespan of energy equipment.

As the energy sector continues to evolve, integrating IoT technologies will play an increasingly vital role in shaping a sustainable future. IoT is at the heart of the energy transition by enhancing grid stability, optimizing renewable integration, and empowering consumers. For energy stakeholders, investing in IoT is a technological upgrade and a strategic move towards a resilient, efficient, and sustainable energy framework, promising a brighter future for global energy systems.

The post How IoT Is Revolutionizing the Energy Transition appeared first on IoT Business News.

The topic of eSIM and remote SIM provisioning has been at the forefront of many of the discussions related to cellular-based IoT connectivity in recent years. Typically the focus has been on in-field provisioning, but in the last few months a new approach has started to emerge, in the form of In-Factory Profile Provisioning (IFPP) involving the secure loading of mobile network SIM profiles during the manufacturing and/or order fulfilment process. In February, Transforma Insights and Kigen published a Position Paper ‘In-Factory Profile Provisioning (IFPP): new eSIM approach drives profitability and improves product performance in connected electronics manufacturing’ which examines the concept of IFPP as an emerging approach to remote SIM provisioning for IoT connected devices. In this article Matt Hatton explores the seven key reasons why manufacturers of connected devices may want to make use of IFPP.

What is IFPP?

Before we get onto the reasons why companies may wish to make use of In-Factory Profile Provisioning it is worth briefly explaining what it is.
IFPP is an extension to the idea of remote SIM provisioning (RSP) which has been quite widely discussed. With the increasing prevalence of soldered eSIM chips and in future iSIMs, as well as increasingly strict rules about network localisation in many countries, it was necessary to develop the capability to change the SIM profile through a mechanism other than physically swapping out SIM cards. That mechanism is Remote SIM Provisioning (RSP), i.e. over-the-air switching of profiles on the SIM card without needing to access it physically. As well as some non-standard and pre-standard approaches, the GSM Association developed a set of standards for the eSIM/RSP architecture: SGP.02 (“M2M”) introduced in 2014, SGP.22 (“Consumer”) in 2016, and now SGP.32 (“IoT”) which was introduced in 2023 and with some final standardisation to be completed.

However, that isn’t the end of the story. There is another scenario in which SIM provisioning might be more effectively supported: to set the initial SIM profile(s) during the manufacturing process. This In-Factory Profile Provisioning (IFPP) is aimed specifically at a particular type of use case: the secure loading of SIM profiles during the manufacturing and/or order fulfilment process based on characteristics such as the device capabilities or the geographic location into which it is expected to be deployed. And the GSM Association has also been active here, working on the SGP.41 specifications for an IFPP standard.

The way in which IFPP works is to allow the manufacturer to hold a digital inventory of Mobile Network Operator (MNO) eSIM profiles and integrate them by way of a profile loader in the manufacturing line, which will apply the next appropriate profile according to pre-established parameters. Typically it will be flashed to the device at the same time as firmware/software loading or when firmware is updated during personalization before the device ships.

Why use IFPP?

As part of the aforementioned Position Paper, Transforma Insights identified seven key characteristics of IFPP that make it stand out as particularly beneficial for manufacturers of connected devices. These are benefits both from IFPP specifically and remote SIM provisioning more broadly.

1. Inbound logistics

Historically, manufacturers needed to maintain an inventory of SIM cards, which may have a lead time stretching into months. Customer orders could not be fulfilled until SIM cards were delivered to the factory, leading to potential delays. In many cases the manufacturer would have to pay for the SIM cards before they arrived in the factory. With IFPP, SIM profiles are used only at the point at which they are required and they can be deployed instantly.

2. Manufacturing process time

Removing the need to manually handle and fit a plastic SIM card into the device eliminates a mechanical step from the manufacturing process, reducing labour cost and speeding up production, which is clearly critical in volume manufacturing.

3. Manufacturing flexibility

Production lines can be adapted automatically and instantaneously to different requirements for SIM profiles, simply by changing the parameters sent to the profile loader. In some cases, manufacturers opt to create generic stock to which a profile is downloaded once the order is received, typically alongside the buyer’s firmware variant. IFPP gives the flexibility to use either approach.

4. Outbound logistics

The use of IFPP removes the need to support multiple SKUs, making distribution logistics more efficient. A manufacturer might easily have 2-3 profiles from major operators for a product in North America, but in Europe, this is 26-30 due to having one operator per country. IFPP simplifies this as production runs can be managed to ensure the right connectivity is selected and meets all operator testing requirements within the process time. We should note that full flexibility is delivered through in-field provisioning, i.e. changing the SIM profile after deployment. IFPP can be deployed in conjunction with in-field provisioning.

5. Out-of-the-box working

Because the device has been pre-configured with the correct profile at time of manufacturing it will automatically attach to the right network rather than having to spin through a bootstrap profile and localising. This provides an improved service for the customer and reduces some of the overhead for MNOs/MVNOs because there is no longer a requirement to support records for short-term bootstrap profiles on the Home Subscriber Server (HSS).

6. Power saving

The power saving benefits are particularly important for many use cases, specifically those devices that rely on lifetime battery power. The power requirement of SIM provisioning – in terms of the volume of data and authentication messages – can use up quite a significant portion of a battery; up to 15% in some cases. This can have a significant impact on the lifespan of devices that had been optimised to be ultra-efficient in their frequency and volume of data delivery. This is particularly relevant for applications using Low Power Wide Area (LPWA) technologies such as NB-IoT, which are typically highly power optimised, for instance in smart gas and water metering.

7. Sustainability

The use of plastic SIM cards involves the production of quite large quantities of waste plastic. At the point of production card bodies are discarded in large numbers. Furthermore, if any network change might need to be initiated, the SIM related to the previous network would be discarded and replaced with a new physical SIM card. There is no such waste with eSIM.

The post In-Factory Profile Provisioning (IFPP): 7 reasons why it makes sense for connected device makers appeared first on IoT Business News.

IoT Analytics, a leading provider of market insights and strategic business intelligence for the Internet of Things (IoT), AI, Cloud, Edge, and Industry 4.0, today has published its latest research on the global cellular IoT module and chipset market.

The report reveals that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, our research projects the market to climb back to near 2022 levels in 2024. The research article further delves into the evolution of cellular IoT modules and chipsets with the latest technological innovations in smart and AI-enabled cellular IoT modules as key drivers for the project growth.

Key insights:

• The latest update to the Global Cellular IoT Module and Chipset Market Tracker and Forecast shows that shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023; however, the tracker projects the market to climb back almost fully to 2022 levels in 2024.
• The rise of smart and AI-enabled cellular IoT modules, which enable data processing and decision-making near or at the edge, is helping to drive this projected growth.
• AI is not the same across the board—the capabilities of AI-enabled cellular IoT modules vary between low, medium, and high based on the speed of AI inference, typically driven by the chipsets used.

Key quotes:

Satyajit Sinha, Principal Analyst at IoT Analytics, remarks “IoT devices are evolving beyond connecting devices and expanding to analyzing the data they produce to make swift, informed choices. As a result, there is a growing need for more computational power and intelligence, especially at the edge closer to the data generated. This trend is also apparent in the cellular IoT field, where integrating AI with cellular IoT modules and chipsets leads to more autonomous decision-making. It also minimizes data transmission over cellular networks, reducing bandwidth and costs. On-device AI models powered by NPUs enhance this capability by enabling smart decision-making at the edge.”

Cellular IoT module market update

Global shipments of cellular IoT modules and chipsets dropped 16% year-over-year in 2023, according to the updated Global Cellular IoT Module and Chipset Market Tracker & Forecast (Q1/2024 Update). Two factors contributed to this decline:

1. Inventory optimization: Initial supply shortages caused by the COVID-19 pandemic and trade tensions around the same time led to manufacturers overordering modules and chipsets in 2021 and 2022, resulting in a surplus of these on the market. To address the surplus in 2023, manufacturers prioritized using existing modules and chipsets, thus delaying new orders.
2. Economic uncertainty: Inflation, rising interest rates, recession fears, and renewed US–China trade tensions have created a cautious investment climate, impacting new orders.

Fortunately, corporate executives appear to be easing their economic concerns, which could lessen the impact of the second factor as supply rebalances with demand. As this balance is achieved, cellular IoT module and chipset shipments are expected to rebound in the near term and are forecasted to grow at a 22% CAGR until 2027, with the slump of 2023 almost fully eradicated by the end of 2024.

Also likely to help rejuvenate this market is the rise of smart and AI-enabled cellular IoT modules—technologies that leverage embedded computational resources to execute advanced data analysis or even AI inference directly on IoT devices. Together, shipments of these advanced modules are forecasted to grow at a CAGR of 76% until 2027.

Smart and AI-enabled cellular IoT modules represent the latest frontiers of cellular IoT connectivity—the latest interactions of cellular IoT technology operating alongside their more basic, yet still quite capable, predecessor modules worldwide. Below, we will look at the evolution of IoT modules and chipsets and delve further into AI-enabled cellular IoT modules, including a look at their various processing capabilities and some applications for these intelligent modules.

Evolution of IoT cellular modules

The evolution of IoT cellular connectivity can be seen as 3 overlapping generations: legacy, smart, and AI-enabled.

“AI-driven productivity is inevitably evolving as an essential to extend the capabilities of IoT devices, significantly [improving] operational efficiency by enriching the IoT device with edge computing.” – Eden Chen, General Manager of MC BU at Fibocom

In the beginning: Legacy cellular IoT modules

Legacy cellular IoT models have been around for nearly two decades, simply providing connectivity for IoT devices to send and receive data from other locations. They include a cellular chipset/baseband to connect to a specified cellular technology, e.g., 2G, 3G, 5G, or NB-IoT.

In 2023, legacy cellular IoT modules comprised 96% of global cellular IoT module shipments. While shipments of these modules are forecasted to grow at a CAGR of 18% until 2027, their share of global cellular IoT module shipments will begin to give way to smart and AI-enabled cellular IoT modules, as discussed below.

Example of a legacy cellular IoT module

An example of a legacy cellular IoT module is the EM9190 5G New Radio (NR) Sub-6 GHz Module from Sierra Wireless, a Canadian wireless communications equipment manufacturer. This module enables devices to connect to 5G networks with 4G and 3G fallback when 5G is unavailable. Sierra Wireless announced the EM91 series of these legacy modules in August 2020, which is fairly recent; however, this reflects that legacy cellular IoT modules are still in demand when edge processing is unnecessary.

*Note: US-based semiconductor and IoT systems provider Semtech acquired Sierra Wireless in January 2023.

A move toward the edge: Smart cellular IoT modules

Smart cellular IoT modules have been on the market for nearly a decade. In addition to providing the connectivity capability found with their legacy counterparts, these smart modules include powerful CPUs and GPUs for on-device data processing. They can also support operating systems like Linux or Android to enable advanced functions and multimedia capabilities.

In 2023, these smart modules comprised 2% of global cellular IoT module shipments; however, the tracker forecasts this number to rise to 10% by 2027, with a CAGR of 79%.

Example of a smart cellular IoT module

An example of a smart cellular IoT module is the CQS290 Smart Cellular IoT Android Module from US-based cellular IoT module manufacturer Cavli Wireless. Cavli announced the unveiling of this module at the India Mobile Congress in October 2023. This LTE Cat 4 module, with Android 12, runs on an ARM Cortex A53 quad-core processor and has a built-in Adreno 702 graphics processing unit (GPU).

Intelligence at the edge: AI-enabled cellular IoT modules

AI-enabled cellular IoT modules are relatively newer than their legacy and smart counterparts, having been on the market for over 5 years. Along with the connectivity capabilities of the other types of cellular IoT modules, AI-enabled versions include NPUs, TPUs, PPUs, or other dedicated parallel-processing chipsets (e.g., GPUs) for AI inference.

While still in its early stages, AI and cellular IoT convergence holds immense potential to revolutionize industries. Integrating AI directly into IoT modules means AI inference can occur at the edge, allowing for rapid and intelligent decision-making at the edge. This reduces data transmission over cellular networks, saves bandwidth and costs, and facilitates immediate, autonomous decision-making for time-sensitive applications. Further, embedding AI chipsets within connectivity modules can save space and streamline the form factor of IoT devices. In all, these modules are evolving from mere data communication enablers to intelligent edge nodes capable of handling certain workloads independently.

In 2023, AI-enabled cellular IoT modules comprised 2% of global cellular IoT module shipments. The tracker forecasts that by 2027, this will grow to 9%, with a CAGR of 73%.

Example of an AI-enabled cellular IoT module

In November 2023, China-based wireless communications modules vendor Fibocom announced the release of its SC228 LTE smart module, which is powered by Qualcomm’s SM6225 (aka Snapdragon 680) SoC. With its 8 processing cores (4 x A73 at 2.4GHz and 4 x A53 at 1.9GHz), the SC228 is designed to handle AI algorithms, such as image processing algorithms. It is geared toward industrial IoT, smart retail, in-vehicle infotainment, and similar applications. The system comes with Android 14 but is upgradable as new software develops. For connectivity, it supports 4G LTE, 3G, WiFi, and Bluetooth.

Capabilities and applications of AI-enabled cellular IoT modules

AI is not the same across all applications. Within AI-enabled cellular IoT modules, there are varying processing capabilities based either on the needs of specific applications or the limitations of the hardware. IoT Analytics generalizes these modules’ capabilities into three categories: low, medium, and high.

1. Low AI capability

Cellular IoT modules with low AI capability conduct AI inference at less than 5 trillion (or tera) operations per second (TOPS), the standard measure of AI performance based on the number of computing operations an AI chip can handle in one second. Common applications of these modules include:

• Acoustic event detection
• Gesture/Activity recognition
• Voice ID/ Keyword spotting

These low AI capability modules comprised 59% of global AI-enabled cellular IoT module shipments in 2023. While the tracker projects the number of shipments of these modules to grow at a CAGR of 30% until 2027, cellular IoT modules with medium and high AI capabilities are expected to grow faster.

Example of a cellular IoT module with low AI capability

Fibocom’s SC138-EAU module features a Qualcomm QCM6125 SoC with an AI engine capable of 1 TOPS.

2. Medium AI capability

Cellular IoT modules with medium AI capability conduct AI inference at 5–10 TOPS. Common applications for these modules include:

• Human detection
• Vehicle detection
• People counting
• Face detection

In 2023, these medium AI capability modules comprised 36% of all global AI-enabled cellular IoT module shipments. The tracker projects that the shipment of these modules will grow at a CAGR of 102% until 2027.

Example of a cellular IoT module with medium AI capability

Quectel’s SG-530C-CN module hosts a UNISOC P778 SoC, which contains an NPU and is capable of 8 TOPS.

3. High AI capability

Finally, cellular IoT modules with high AI capability conduct edge AI inference at over 10 TOPS. Common advanced applications for these modules include:

• AI-driven predictive maintenance
• Enhanced decision-making with advanced analytics
• AI-enhanced driver safety solutions
• Real-time monitoring for drowsiness and distractions
• Comprehensive safety analysis
• Intelligent voice assistance

According to the tracker, these high AI capability modules comprised 5% of all global AI-enabled module shipments in 2023. The tracker forecasts the shipments of these modules to grow at a CAGR of 128% until 2027.

Example of a cellular IoT module with medium AI capability

MeiG’s SRM930 module bears a Qualcomm QCM6490 SoC, which includes Qualcomm’s 6th Gen AI Engine capable of reaching an AI performance of 12 TOPS.

Analyst takeaway

IoT is evolving beyond mere connectivity—it now encompasses connecting devices, understanding the data they generate, and making fast, informed decisions based on this data. As such, computing power and intelligence are becoming increasingly essential, particularly closer to where data is generated—at the edge. Thus, it is beneficial to have a dedicated chipset, such as a GPU or NPU, that can be used for AI inference directly on IoT devices, whether embedded in the printed circuit board (PCB) or as a component within the main processor.

Cellular IoT modules are undergoing a similar evolution. Although still in the early stages, integrating AI with cellular IoT promises to transform various industries. However, the core technology is driven by chipset companies like Qualcomm, Sony Altair, and UNISOC. Other chipset companies like MediaTek and ST may enter this market soon. So far, as seen above, vendors are predominately using Qualcomm chipsets equipped with AI engines that utilize the chipsets’ CPU, GPU, or NPU components.

With the rise of AI-enabled cellular IoT modules, two trends are emerging that are worth watching:

• AI-enabled 5G modules in automotive: The adoption of AI-enabled cellular modules focused on automotive applications, especially with 5G connectivity, is expected to accelerate. By 2027, AI-enabled 5G modules for automotive applications are projected to constitute 21% of all AI-enabled cellular module shipments.
• AI in cellular LPWA modules. So far, most of the modules are focused on standard 5G and 4G technology (with 2G and 3G as fallbacks). However, cellular LPWA modules are already entering the scene. For example, the Sony Altair ALT1350 is a low-power, LTE-M/NB-IoT SoC equipped with AI capabilities for low-power acceleration. This chipset is designed for edge processing and tiny ML model inference, opening doors for AI-enabled modules in the cellular LPWA segment.

Disclosures

Companies mentioned in this article—along with their products—are used as examples to showcase market developments. No company paid or received preferential treatment in this article, and it is at the discretion of the analyst to select which examples are used. IoT Analytics makes efforts to vary the companies and products mentioned to help shine attention to the numerous IoT and related technology market players.

It is worth noting that IoT Analytics may have commercial relationships with some companies mentioned in its articles, as some companies license IoT Analytics market research. However, for confidentiality, IoT Analytics cannot disclose individual relationships.

The post The rise of smart and AI-capable cellular IoT modules appeared first on IoT Business News.

IoT Analytics has published a new analysis that highlights 10 emerging IoT trends driving market growth.

This analysis is derived from the comprehensive “State of IoT – Spring 2024” – a report on the current state of the Internet of Things, including market updates and projections, the latest trends, market sentiments, investments, M&As, industry expert opinions, and more.

Key Insights:

• According to the latest State of IoT – Spring 2024 report, IoT remains a top-three corporate technology priority.
• While AI has surpassed IoT in corporate prioritization, combining IoT and AI is on the rise and seen as a tailwind for the $236-billion IoT market rather than a disrupter.
• IoT Analytics identified 40+ current IoT market trends in this research, 10 of which are shared below.

Key Quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks:

“In 2023, the IoT market demonstrated significant resilience among economic fluctuations and geopolitical tensions. We now estimate a robust growth of 17% per annum through 2030. This growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity within the IoT sector.”

IoT remains a top corporate priority on the back of the current AI wave

IoT remains a top priority. IoT remains a top-three corporate technology priority while AI has taken over as the top technology priority, according to the latest 148-page State of IoT – Spring 2024 report. In recent surveys from PWC, KPMG, and BCG, respondents ranked IoT second or third after AI in terms of investment prioritization for emerging technologies, with AI coming in first across the board.

AI is a tailwind for IoT. The latest research finds that the growth of AI is a strong tailwind for the $236-billion IoT market, as companies are gaining interest in both AI and IoT within their organizations. One indication is from IoT Analytics’ analysis of company earnings calls: since Q3 2022, the mention of these two technologies in the same earnings call rose by 61%.

40 current trends identified by the IoT Analytics team. IoT Analytics’ market analysis relies on the valuable findings of its research analyst team and input from industry experts and advisors. These professionals contributed greatly to the spring 2024 State of IoT report, which showcases nearly 40 IoT market trends, along with IoT market data, recent IoT news and developments, and the performance and activities of IoT companies. The analysis shows that AI is not the only trend that will help drive the IoT market—10 of the trends discussed in our report can be found below.

IoT market expected to continue its growth path. IoT Analytics assesses that the 10 trends discussed in this article (among many more) will contribute to an IoT market CAGR of 17% until 2030, which is a cautious downward revision from the forecasted 19% CAGR in early 2023 but nonetheless a testament to the strength of the technology and its impact in a variety of markets.

CEO quote: “In 2023, the IoT market demonstrated significant resilience among economic fluctuations and geopolitical tensions. We now estimate a robust growth of 17% per annum through 2030. This growth is fueled by an increase in connected assets and corresponding investments in AI and cybersecurity within the IoT sector.” – Knud Lasse Lueth, Chief Executive Officer

Spring 2024 macro environment outlook: Lingering economic uncertainty, but IoT remains a key investment

Inflation coming down. The high inflation rates that we saw in 2023 seem to be subsiding. The Euro area inflation estimate for January 2024 was 2.8%, down from 2.9% in December 2023. In the US, consumer inflation has greatly eased from its peak of 9.1% in June 2022; it currently stands at 3.2% in February 2023. In Asia, some countries witnessed their lowest inflation levels since 2022.

Muted global economic growth projections. However, despite inflation progress, global economic growth projections are still below the historical annual average. The IMF forecasts a 3.1% global economic growth in 2024 and a 3.3% growth in 2025. Additionally, by December 2023, China—the second-largest economy and the country with the most connected devices—experienced its longest deflation streak since 2009, with prices declining for the third consecutive month. On the flip side, India’s economy has been outperforming the rest of the world.

IoT markets affected, but positive sentiment shows. The IoT market has seen positive economic news lately. Going into 2024, the Global Supply Chain Pressure Index is back to its long-term average, indicating a normalization of supply chains after stormy times during the pandemic. Additionally, business sentiment around IoT appeared overall positive, and many IoT companies reported significant year-on-year growth in both revenue and gross margin within the IoT sector in Q4 2023 (e.g., Supercom, Lantronix, and Globalstar). Unfortunately, some IoT companies have reported revenue declines and market weakness.

Analyst quote: “India has emerged as the ‘new China’ in terms of growth outlook.” – Philipp Wegner, Principal Analyst—Data

With this market backdrop, the following is a list of 10 notable IoT and tech-related trends and opinions identified by the IoT Analytics team as part of the research for the report with accompanying commentary:

Trend 1: Semiconductor companies invest in embedded chipset security

Semiconductor companies are increasingly investing in embedded chipset security to address the growing security threats IoT devices face. Securing hardware at the chipset level with secure elements and physical unclonable functions (PUFs) can help protect data flowing from edge devices to the cloud.

Example: The ecosystem of US-based semiconductor company Intel is growing with security partners like US-based digital authentication company Intrinsic ID. In February 2024, Intel Foundry added Intrinsic ID to its Accelerator IP Alliance program, aiming to ensure the availability of hardware-based root-of-trust solutions for the Foundry’s members. Accepting that security and reliability are valuable for applications, the Foundry opened access to Intrinsic ID’s QuiddiKey X00 product family of root-of-trust (RoT) solutions, which use standard SRAM as a PUF to generate a hardware RoT without needing additional security-dedicated silicon.

Analyst quote: “Semiconductor companies are at the forefront of tackling [the growing security threats IoT devices face], focusing on investing in embedded chipset security, as hardware forms the foundational layer.” – Satyajit Sinha, Principal Analyst – Connectivity and hardware

Trend 2: Industrial automation hardware is becoming more intelligent with the integration of AI chipsets

With the advancement in AI technology, companies are now looking to leverage AI at the edge, increasing the demand for real-time data analytics also at the edge. AI chipsets are also becoming smaller in size while growing in power. This trend has led to the emergence of IPCs and gateways embedded with AI chipsets, resulting in edge AI equipment that can perform parallel computations and train algorithms with very low computational response latency.

One benefit of using AI chipsets at the edge is the acceleration of data processing directly on the industrial equipment. This, in turn, reduces network traffic and enhances security, as the amount of data going to the cloud for processing is reduced.

Example: At SPS in November 2023, Germany-based automation and digitalization manufacturer Siemens presented its SIMATIC IPC520A Box PC embedded with 6-core NVIDIA Carmel for edge capabilities and NVIDIA Jetson Xavier NX GPU for A, making it suitable for AI-oriented operations. The IPC520A is designed to work seamlessly with AI-based applications across various industries, including factory automation and logistics.

Analyst quote: “Advancements in AI chipsets specifically designed for the edge are noteworthy. Embedding AI chipset in edge hardware such as IPCs and gateway is bringing decision-making closer to the edge and opening doors to new IoT applications such as machine vision.” – Kalpesh Baviskar, Market Analyst—Connectivity and hardware

Trend 3: The race for generative AI solutions in manufacturing has begun

Many industrial generative AI (GenAI)-based solution showcases are popping up. Vendors in the industrial and manufacturing space are racing toward developing GenAI-based solutions around coding, troubleshooting/support, operational analytics, and generative design, among others.

Examples: The following are just two of the six showcase examples found in the State of IoT – Spring 2024 report:

• At SPS 2023, Germany-based automation technology company Beckhoff showcased its TwinCAT Chat for the TwinCAT XAE engineering environment. The TwinCAT Chat Client enables AI-supported engineering to automate tasks such as the creation or addition of function block code, code optimization, documentation, and restructuring.
• In November 2023, Canada-based industrial AI software company Canvass AI announced the next evolution of its industrial AI software with Hyper Data Analysis. Through the use of GenAI, the Canvass
  AI software now incorporates learnings from text and visual-based data—adding it to production data streams—to advance traditional time-series-based AI insights for applications such as visual inspection, predictive maintenance, and quality within the process industries.

Analyst quote: “15 industrial automation and related vendors at SPS 2023 told us that GenAI is currently one of their top technology priorities. Moreover, we observed that these GenAI-based solutions are mostly in the stage of showcasing their capabilities rather than being widely available to the public. We believe this is to evolve in the coming months when vendors will go ‘live’ with these products for purchase.” – Fernando Brügge, Senior Analyst—Industrial IoT and AI

Trend 4: Generative AI has a positive (not negative!) impact on the manufacturing workforce

The race to integrate GenAI solutions in manufacturing—and how it differs from other technologies so far—revolves around the speed of adoption and the level of investment in the technology. Within three months of its public launch, ChatGPT reached an estimated 123 million users, an incredible feat for a new type of product. Additionally, soon after ChatGPT’s launch, Microsoft made a $10 billion investment in OpenAI, helping increase ChatGPT’s profile. This investment also showed the seriousness big tech companies like Microsoft are placing in this technology, leading companies across industries to question how they could leverage GenAI in their processes.

Adoption of new technology in manufacturing is often associated with negative impacts on the workforce. However, GenAI adoption in manufacturing is expected to boost employment and upskilling, shifting focus from automation to strategic growth. With GenAI contributing potentially $2.6–$4.4 trillion to the global economy annually, according to McKinsey, manufacturers are likely to deepen their investment in AI technologies.

However, AI’s impact on the workforce appears counterintuitive to common automation narratives. According to the Manufacturing Leadership Council, 32% of manufacturers anticipate an increase in headcount due to AI, suggesting that AI will create new roles and require upskilling rather than just automating existing ones. With 96% of manufacturers projecting increased investment in AI, there is a clear trend toward embracing AI for cost savings, growth, and revenue generation.

Emerging roles will likely include AI strategy managers and data specialists, reflecting a shift toward higher cognitive work.

Advisor quote: “The urgency for upskilling is underscored by the current lack of a dedicated AI training budget in 65% of manufacturing firms, signaling a potential increase in investment in human capital.” – Jeff Winter, Industry 4.0 expert and advisor

Trend 5: Companies are in danger of neglecting tech adoption basics in the rush to generative AI

GenAI is everywhere. Vendors are looking for ways to implement it in their products or to create new ones, and end users are eager to adopt. This rush, however, is not always helpful when it comes to adopting new technologies. The hype can often shift the mindset of adopters and vendors alike from “What technology should I use to alleviate X pain point?” to “How should I use this technology to alleviate some (sometimes non-existent) pain point?”

In almost all the surveys that IoT Analytics conducts—be it about IoT use case adoption, Industry 4.0, IoT software, or similar—“having a set goal” is always on the list of success factors that respondents mention. This is often forgotten whenever a new technology promises to “change the way we work” (the metaverse, for example).

Analyst quote: “[AI] should be treated like any other technology. First, think of the why, who, and how before deciding on implementing it. Second, if you are a (software) vendor, you should also keep in mind that being fast to innovate is not always the most important factor to keep your customers happy.” – Dimitris Paraskevopoulos, Senior Analyst—Quantitative data

Trend 6: Marketplaces are gaining in importance for technology procurement

Companies and sellers alike are embracing the subscription-based economy and seeking to simplify the procurement process. In January 2024, IoT Analytics published an article delving into the rise of B2B marketplaces, noting that B2B marketplaces are the fastest-growing procurement channel for software.

Analyst quote: “Cloud hyperscaler marketplaces currently lead in cloud-based software spending since many businesses have already committed cloud spending that can be utilized to procure software from these platforms.” – Justina-Alexandra Sava, Market Analyst—Software

Trend 7: Data fabric is emerging as an advanced evolution of data lakes

Though a relatively new term, data fabric describes a comprehensive data integration and management framework. It encompasses architecture, management tools, and shared data sets and is designed to assist organizations in handling their data.

Data fabrics differ from data lakes in that they go beyond storing raw data and from data warehouses in that they handle only processed or refined data. A core benefit of data fabrics is they offer a cohesive, consistent user interface and real-time access to data for all members of an organization, regardless of their global location.

Examples:
In 2023, several large data management vendors either upgraded their already existing data fabric solutions or launched new solutions:

• In February, US-based data integration platform provider Talend—one of the early users of the term data fabric—announced upgrades to their Talend Data Fabric solution, which was initially launched in 2015.
• In May 2023, US-based technology and software company Microsoft introduced Microsoft Fabric and launched it in November.

Analyst quote: “Given the increase in data complexity because of the exponential growth in big data, propelled by hybrid cloud, AI, IoT, and edge computing, there seems to be a good opportunity for vendors [offering data fabric].” – Mohammad Hasan, Market Analyst—Software and cloud

Trend 8: Hyperscalers pivot their edge strategies to innovate and secure their IIoT market position

Cloud providers are strategically adapting to the evolving IIoT market. In recent developments within the IIoT landscape, important shifts have occurred among major cloud service providers, with more focus on edge and containerization strategies.

Examples:

• Microsoft pivots its Azure IoT strategy toward Kubernetes. Microsoft has notably redirected its strategy toward Kubernetes, a move signaling a pivot within its Azure IoT Operations offering. This transition, accompanied by organizational restructuring and the discontinuation of the Azure Certified Device Catalog, highlights how Microsoft’s IoT strategy seems to evolve as technology and market dynamics shift.
• Google ends IoT Core but keeps Manufacturing Connect via Kubernetes. Similarly, Google’s decision to terminate its IoT Core offering in August 2023 has prompted attention. Despite this closure, Google’s IIoT solution, Manufacturing Connect, remains viable through its Kubernetes-compatible architecture, a strategic alignment reflecting the company’s technical prowess in this domain.
• AWS boosts IIoT investment with Sitewise and Monitron enhancements. AWS seems to double down on the IoT edge, which is particularly evident in the revitalization of Sitewise and other offerings like Monitron.

Advisor quote: “As the IIoT market evolves, cloud giants like Microsoft, Google, and AWS are moving further to the edge by embracing Kubernetes and enhancing edge computing capabilities. Their strategies revolve less around serving individual use cases themselves but rather participating in the edge (software) platform layer, which serves as the basis of digitalization for their partners and customers.” – Matthew Wopata, Edge solutions expert

Trend 9: Industrial vendors are strongly investing in DataOps solutions

Several vendors are investing in industrial DataOps solutions to tackle data integration and analysis challenges.
Industrial DataOps is an approach to data integration focusing on enhancing data quality through contextualization and modeling. This approach is experiencing growing attention within the industrial connectivity space.

Examples:

Some of the key vendors that offer industrial DataOps for data contextualization and modeling include:

• New vendors: Cognite – CDF, HighByte – Intelligence Hub, Prosys – OPC UA Forge, Litmus – Litmus Edge, Element – Unify, and Crosser – Flow Studio
• Industrial incumbents: ABB – Ability Genix, Aveva – PI System, Aspentech – Inmation, GE Digital – Asset Modeler, and Halliburton – DecisionSpace 365

Analyst quote: “In the world of AI, OT data stands as the cornerstone. It’s the quality and context of this data that truly empowers insights. We are seeing vendors aggressively advance DataOps tools for modeling and contextualization, with both new entrants and established OEMs perfecting their solutions. This concerted effort underscores the pivotal role of enhancing the quality of data from varied assets/software in unlocking digital transformation’s full potential.” – Anand Taparia, Principal Analyst—Industrial IoT

Trend 10: Robots charged per hour are starting to replace manual labor due to labor shortage

Manufacturing companies can benefit from equipment as a service (EaaS) by replacing labor-related operational expenses with another type of operational expense: robotics as a service (RaaS). RaaS is a relatively new business model, where a robot is provided by a machine builder on an outcome-based basis (paying per parts produced with the equipment) or runtime basis (paying per hours of equipment used) instead of as a direct purchase.

Example: US-based truck trailer chassis manufacturer Cheetah Chassis chose to hire welding robots per hour and explained that it could not find enough welders to fulfill demand. Its CEO, Garry Hartman, explained that it had trialed robotics before, but it was unsuccessful because it did not have the capacity to program and service robots. With RaaS, Cheetah Chassis can now enjoy the benefits of robotics without having to do so because it is provided by the RaaS vendor.

Analyst quote: “Companies are more likely to consume equipment by the hour if they are trying to fill in the gaps in the workforce (which is already paid by the hour) rather than purchasing new equipment.” – Matthieu Kulezak, Senior Analyst—Industrial IoT

Conclusion

The IoT sector is undergoing transformative changes. The new 148-page State of IoT – Spring 2024 report highlights the continuous evolution and resilience of the IoT market, driven by technological advancements and strategic shifts.

The shift of hyperscalers towards edge and containerization strategies, the integration of AI into industrial automation, the advent of generative AI in manufacturing, and the rise of data fabric solutions represent just a few of the dynamic developments redefining the enterprise IoT ecosystem. These trends, alongside other market data in the report, not only reflect the current state of the market but also provide a glimpse at future growth and innovation.

In navigating this landscape, it is crucial for businesses to stay informed and adaptable. With a projected CAGR of 17% until 2030, the potential for growth and transformation in the IoT sector is immense.

The post State of IoT Spring 2024: 10 emerging IoT trends driving market growth appeared first on IoT Business News.

Millions of smart toothbrushes hacked and “turned into secret army for criminals?” Sounds like Hollywood pretense or something born from the collective imagination of today’s security pros and, in this case, it was.

In late January, Swiss publication Aargauer Zeitung wrote an article describing how hackers had launched a distributed denial-of-service (DDoS) attack against approximately 3 million smart toothbrushes. The story claimed damages to be millions of euros. Numerous English-language publications, including ZDNet, Tom’s Hardware and The Sun, picked up the story and reported on the attack.

It wasn’t until a week later that Fortinet, Aargauer Zeitung’s source, clarified that the situation was a hypothetical attack discussed during an interview—blaming a translation error for the misunderstanding. While there has understandably been some fallout over the viral nature of the story, I caution companies from dismissing this scenario entirely.

It didn’t happen, but that doesn’t mean it couldn’t. And while it’s unlikely that a connected toothbrush would cause the chaos outlined in the original Swiss article, it still serves as an important reminder that IoT devices remain a sought-after hacker target.

With that in mind, following are some important considerations to ensure their security:

Enable All Security Features

Many connected devices offer encryption or other additional security features. Too often organizations and consumers fail to enable them, making it much easier for a threat actor to compromise the device.

Strengthen Authentication

Using multifactor authentication (MFA) whenever possible is also an important step as part of a layered approach to IoT security.

Evaluate Unneeded Features

Another best practice is to disable any unnecessary features, as well as ensuring that any older unused devices are disconnected from the network. The latter often have outdated security, which can create a weak point on the network that cybercriminals can easily exploit.

Ensure Devices are Up to Date

Frequently check all IoT manufacturers’ websites for firmware updates and patches. If the smart device has an accompanying app, ensure that the most up-to-date version is in use.

Change the Default Settings

It wasn’t too long ago that many IoT devices were shipped with the same default password as standard—for example, in 2019 600,000 GPS trackers arrived all with 123456 as their password. While manufacturers no longer assign the same credential to all products out of the box, it’s still important to change the password and all other default settings prior to use.

IoT Security Demands Threat Intelligence

Unfortunately, changing a device’s password isn’t enough from an enterprise security perspective. People typically reuse passwords across numerous applications and systems, with one study finding that 72% of individuals deploy the same one in their personal life and nearly half of employees simply change or add a digit or character. Given the high rate of data breaches, all it takes is one attack for these credentials to be available on the Dark Web for threat actors to utilize in subsequent breach attempts.

This is a key reason that threat intelligence is a vital component of any modern IoT security strategy. Organizations need real-time insight into the integrity of the credentials used to secure and access connected devices so that they can take immediate action in the event of a compromise—and prevent any subsequent damages from occurring.

Giving IoT Security Some Teeth

Once the Aargauer Zeitung story was debunked, many articles pointed out that threat actors generally pursue attack avenues more closely linked to monetary gain. And while connected toothbrushes don’t contain financial data, the same can’t be said for enterprise IoT devices used for predictive maintenance, smart energy management, or occupancy monitoring.

As such, the hypothetical attack scenario is a timely nudge to ensure the security of these and other enterprise connected devices. The news media will soon forget about this viral (if untrue) story, but the same can’t be said for hackers’ fixation on smart devices’ security vulnerabilities.

The post Don’t Brush Off the Toothbrush Story: Connected Device Security is A Major Concern appeared first on IoT Business News.

IoT Analytics has published a new analysis focusing on smart meters.

It is derived from the comprehensive “Global Smart Meter Market Tracker”. The tracker includes installed base, shipments and shipment revenues for electricity, gas and water smart meters. The current analysis underscores the global adoption rate of smart electricity meters in 2024, providing an in-depth regional exploration and market forecast.

Key insights:

• By the end of 2023, 1.06 billion smart meters (electricity, water and gas) have been installed worldwide, according to IoT Analytics’ Global Smart Meter Market Tracker 2020–2030.
• Smart meters enable utility service providers across the world to digitalize their distribution infrastructure and services efficiently with near real-time data.
• North America has the most mature smart electricity meter market, with nearly 77% electricity meter market penetration, while Latin America has largely lagged in its adoption of the technology. Some European Union countries and the East Asian region, too, have high rates of smart electricity meter market penetration.
• South Asia, Latin America, and Africa represent a high-growth potential for smart meters, as some regional governments have become convinced of the need to update their aging grid infrastructure and are more actively engaging with smart grid industry stakeholders to develop regulatory policies to drive the adoption of smart meters.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “IoT-based smart electricity meters have become a reality in the world but adoption varies greatly by country and region with countries like Sweden, France, or Canada having completed or nearly completed their roll-outs while others like Germany are yet to start their initiative in a meaningful way. India is likely to see the largest roll-out in the coming years.”

Adarsh Krishnan, Principal Analyst at IoT Analytics, adds that: “Digital transformation is sweeping the utility industry as global service providers’ smart meter deployment, a foundational smart grid technology, exceeds installed base of 1 billion units. While advanced economies embrace feature-rich smart meters, emerging markets focus on more cost-effective solutions for their grid upgrades. Furthermore, future advancements in AI and edge computing will bring greater operational efficiencies and innovative consumer services, creating sustainable and resilient smart grids.”

By the end of 2023, Utility Service Providers (USPs) around the world will have installed over 1.06 billion smart (electricity, gas, and water) meters, according to IoT Analytics’ updated Global Smart Meter Market Tracker 2020–2030. As IoT devices, smart meters are enabling energy and water USPs to build resilience into their operations with near real-time data from their distribution networks. With sustainability and the digitalization of utilities gaining traction worldwide, the installed base of these devices is expected to exceed 1.75 billion by 2030 (CAGR 6%), making the smart meter market a market to watch closely.

Smart electricity meter adoption is far ahead of the adoption of smart gas and smart water meters at this point, though the picture could change by 2030, with smart gas and water meter adoption expected to grow at 10% and 16% CAGR, respectively.

While the tracker provides in-depth coverage of smart electricity, gas, and water meters across 52 countries and 5 regions—including installed base, shipments, revenue, market penetration, and connectivity technology—IoT Analytics plans to offer highlights for each smart meter submarket separately as its own article, starting here with smart electricity meters.

Global smart electricity meter market snapshot

As of late 2023, the smart electricity meter market achieved 43% penetration of the overall global electricity meter market, according to the market tracker.

Electricity grid modernization initiatives started in the late 2000s in Italy and the US and accelerated to national rollouts throughout the EU and APAC regions after 2010. Regulatory policies—supported by financial incentives from regional or national governments—have contributed to this growth, as these policies have encouraged utilities to replace mechanical electricity meters with smart meters to modernize their grid infrastructure.

However, as discussed below, not all parts of the world are modernizing their electricity infrastructure. According to the tracker, North America, Europe, and East Asia have had higher rates of smart electricity meter market penetration, but adoption rates still vary from country to country. Meanwhile, Latin America, Africa, and South Asia have been slow to initiate smart electricity meter projects across the board. Some countries have initiated large-scale smart electricity meter projects in recent years, though project implementation complexity, lack of regulatory policies, and cost hurdles have delayed rollouts in several countries.

Overall, the market for smart electricity meters looks promising, as the Smart Meter Market Tracker forecasts these IoT devices to achieve 54% adoption of the overall global electricity meter market by 2030.

Smart electricity meter market and adoption by region

While the smart meter market tracker shares market data down to the country level, the following are highlights about the smart electricity meter market at the regional level.

North America leads in smart electricity meter adoption

North America has the most mature smart electricity meter market, with nearly 77% electricity meter market penetration by the end of 2023.

In the US, smart electricity meters have 76% penetration in the overall electricity meter market as of 2023, driven by large-scale deployments from investor-owned utilities. Smart meter rollouts in the US are expected to slow down or plateau during the forecast period due to smart meter’s high penetration rate and long product life cycles. As municipalities with smaller budgets and cooperative-owned utilities replace their traditional electricity meters with smart meters, smart meter annual shipments in the US should see marginal growth through the rest of the decade.

Furthermore, the region will get a further boost in smart meter shipments, as Canadian utilities Fortis and Hydro One have announced plans in 2023 to replace their existing AMI with 2nd-generation smart meters.

The APAC region has the second most mature smart electricity meter market, driven by nationwide deployments in China and Japan.

Meanwhile, the APAC region has the largest addressable market for smart electricity meters, with over 1.1 billion electricity metering endpoints. In 2023, the APAC region accounted for almost 60% of the global smart meter installed base and more than 50% of annual smart meter shipments. In 2023, the region achieved a smart meter penetration rate of 49%, largely driven by successful nationwide rollouts in China and Japan. With planned nationwide deployments in Australia, South Korea, India, Indonesia, and Singapore, the region’s smart meter penetration is expected to reach 67% by the end of this decade.

Of note in this region, in 2021, India’s government set an ambitious goal of installing 250 million smart electricity meters by the end of 2025. To execute the implementation strategy, the government of India launched the Revamped Distribution Sector Scheme (RDSS) not only to help financially support regional USP smart meter deployment and maintenance but also to expand the domestic manufacturing capacity to produce smart meters within India. By the end of 2023, India had achieved less than 3% of this goal, making it unlikely for this goal to be met before 2030. That said, by 2030, India is on track to become the single largest market for smart electricity meters in terms of annual shipment and revenue.

Europe comes third in smart meter adoption, though adoption differs greatly by country

Europe had 47% smart electricity meter market penetration across the continent at the end of 2023. France, Spain, Italy, Netherlands, and the Scandinavian countries initiated nationwide rollouts in the last decade, while Greece, Hungary, Poland, and Romania only started their initiatives more recently.

Germany, with over 50 million electricity metering points, has largely lagged in its adoption rate, with under 10% of smart electricity meters deployed to date. However, in early 2023, the government of Germany revamped its 2016 Metering Point Operation Act to speed up smart meter deployments, targeting a complete rollout by 2032. The new law stipulates binding deadlines for USPs with a roadmap that includes 20% rollout by the end of 2025, 50% by the end of 2028, and 95% by the end of 2030 for residential and small business consumers, with targets extending to 2032 for large consumers. However, there is strong market skepticism around achieving these deadlines due to the need for clarity from the government around financial support for USPs, AMI technical specifications, data privacy, and security governance framework.

Saudi Arabia and the UAE lead in the Middle East and Africa region

In the Middle East and Africa region, Saudi Arabia and UAE are leading the way in the implementation of smart meters for electricity. In 2022, Saudi Arabia’s state-owned USP Saudi Electricity Company (SEC) announced the successful deployment of approximately 11 million smart meters over three years. Meanwhile, the UAE, which already has 1.6 million smart electricity meters installed, is expected to complete its nationwide rollout by the end of 2029.

Latin America lags in smart electricity meter adoption

Finally, Latin America has seen the slowest smart electricity meter deployment, largely due to regulatory indecisiveness delaying project rollouts. Uruguay was the first country in the region to mandate a nationwide smart meter rollout, aiming for completion in 2026.

Analyst’s outlook on the electricity smart meter market

Though regional variations persist—with energy USPs in North America, Europe, and East Asia boasting much more mature markets than their counterparts—the regions of Southern Asia, Latin America, and Africa represent a high-growth potential for smart meters. Some key considerations for various stakeholders are as follows:

• Market saturation and marginal growth in advanced economies: The implementation of more advanced and feature-rich 2nd-generation smart meters is already underway or in the advanced planning stages in countries such as Sweden, Italy, Finland, and Canada. This is likely to marginally drive up the average selling price of smart electricity meters.
• Cost sensitivity in emerging markets: In regions such as South Asia, Latin America, and Africa, where penetration rates are lower, some national governments are convinced of the need to upgrade their aging grid infrastructure and are actively engaging with smart grid industry stakeholders to develop regulatory policies and standards to drive the adoption of smart meters. However, these are also cost-sensitive markets where low-cost smart meters are more likely to be successful.
• Smart meter supply chain diversification: Several countries (e.g., Saudi Arabia, Mexico, Brazil, India, and Indonesia) that are initiating large-scale rollouts are stipulating that smart meter OEMs localize the manufacturing of 40% or more of the smart meter demand.
• Regulatory policy uncertainties: Policy indecisiveness creates complex and uncertain environments for smart meter stakeholders, hindering innovation and investments that subsequently delay smart meter deployments, as seen in countries such as Brazil, India, Mexico, and South Africa.
• Future innovations and market trends: Innovations in ICs, edge computing, and AI (TinyML), as seen in 2nd-generation smart meters, may help reduce strain on communication networks, improve real-time responses to grid fluctuations, build resilience, and enhance data security and privacy.

Based on the Global Smart Meter Market Tracker 2020–2030, the traditional USP industry, once considered a laggard in adopting new technology innovations, is leading the digital transformation market with more than a billion smart meters and accelerating its digital footprint.

IoT Analytics will closely monitor this evolving USP industry and technology landscape to provide in-depth analysis and actionable insights into this market. Its next report on energy utilities (expected in Q2 2024) will provide a deep dive assessment of USPs in 10 countries to identify key trends in smart grid programs, such as distribution automation, green energy integration, and EV charging infrastructure.

The post Smart electricity meter market 2024: Global adoption landscape appeared first on IoT Business News.

Although the initial adoption of IoT was fairly slow, it has fast become vital to businesses over the last five years. IoT is enabling businesses to increase revenue, create operational efficiencies and deliver new product lines in new markets, our research found. In today’s always-on and fully integrated world, fast and reliable connectivity between all devices is an extremely important business enabler, and those who invest in their IoT strategy are seeing the benefits. It is also a key driver behind digital transformation and holds the keys to allowing businesses to evolve and adapt to modern, digital-first practices. However, there is still a lot that can be done to fully unlock the potential of IoT across almost every industry.

Smart connectivity is the gateway to automation

As the adoption of IoT becomes more widespread, and IoT matures as a technology, traditional implementations of simple networks and connected devices will not be intelligent enough. The beauty of having all of your devices fully connected is that it provides a wealth of real-time data, which enable the system to be more proactive in its response to changing conditions. This does, however, require more processing and analysis on the part of the network, meaning a much more intelligent system is needed.

To fully take advantage of everything that IoT can offer, businesses should implement a ‘smart connectivity’ solution. As an example of this, take a system that monitors the heartbeat of a patient. While this could easily be set up to alert a healthcare professional once the heart rate exceeds a certain limit, this would not allow enough time for a doctor or nurse to respond, depending on the severity of the patient’s condition.

With smart connectivity, this system could be vastly improved. To link the intelligence in the IoT device to the health monitoring analytics and operational dashboards in the cloud, you need intelligent or ‘smart’ connectivity built into the device that enables secure, compliant, and resilient device-to-cloud connection. This would allow the system to be much more proactive, giving it the data and analytics it needs to respond to events before they happen, as opposed to being reactive. In terms of healthcare, the real benefit of this could be saving lives.

There is also a growing need for remote patient monitoring (RPM), which allows patients to be discharged earlier and be safe in the knowledge that highly accurate data about their health will reach their healthcare providers. An excellent example of this being put into practice is Telli Health’s RPM offering, which uses Eseye’s multi-network eSIM connectivity solution, as this is much more reliable than Bluetooth. This reliability is further bolstered by not having to rely on only one network, which also allows for greater geographic coverage for connection.

Keeping up with digital transformation

Organisations strive to take advantage of modern technology as much as possible, in a process dubbed ‘digital transformation’, and IoT is one of the main drivers of this in many industries. One sector that benefits massively from the adoption of IoT is logistics.

Logistics touches every industry that deals with physical goods, and it plays a huge role in the day-to-day activities of most organisations. Due to the highly decentralised nature of the supply chain, IoT technology is a perfect match to solve some of the industry’s biggest challenges.

One main benefit is enhanced visibility and control over the disparate elements that make up the supply chain. A fully integrated network of devices allows companies the ability to monitor their assets, assess risks, and respond quickly to any issues that may arise.

IoT is also a great enabler for automation. Many logistics companies strive for a greater level of self-sufficiency, as it reduces human labour and associated costs, as well as reduces the margin for human error. However, to achieve a high degree of automated processes across the supply chain, it is essential to connect the devices connected to a central network and each other.

The past year has also seen an explosion in the usefulness of AI, and companies want to get as much out of this emerging technology as they can. This is achieved with a robust IoT solution, as it can be used, for example, in warehouses to manage inventory with automatically updating shelves.

Sustainability into the future

Another way in which IoT can help a business excel is in achieving sustainability targets. There is increasing pressure on organisations to become more efficient and decrease waste, as well as reduce harm to the environment. Through a fully connected IoT solution, techniques such as energy harvesting can be employed to extend the lifetimes of devices, as well as optimise energy use to improve overall efficiency.

IoT is essential for the electric vehicle industry, particularly when it comes to EV charging points. This is needed for practically every function of an EV charger, from payment processing and software updates to collecting user analytics. Unreliable connectivity can lead to delays which reduce the efficiency of charge points, limit customer throughput and – perhaps most concerning in a sector where adoption is still at an early stage – dent customer confidence. The technology pioneered through Eseye’s partnership with InstaVolt proves that these issues can be overcome with the right expertise, connectivity and implementation.

Additionally, as electric vehicles become the standard, any charging points that are installed now will need to be future-proof to be ready for the increasing adoption of EVs. The goal, therefore, is to create charging points that can run for 10 years, or more, with minimal human intervention. Having a low-maintenance, durable charging point network is achievable with IoT.

As we move into 2024 and beyond, businesses must embrace IoT if they want to stay ahead of the curve in a fast-paced and ever-evolving world. Choosing the right connectivity partner to provide high-quality service and maximum flexibility will ensure operational resilience and efficiency into the future, regardless of industry. Above all, reshaping industries with IoT can benefit not only the business but also the environment.

The post Practical Applications of IoT in Business appeared first on IoT Business News.

As our world becomes increasingly connected, technology is often portrayed as a villain, sent to invade our privacy and steal our jobs; however, the real narrative of Internet of Things (IoT) is quite different.

“IoT for Good” – the application of IoT technologies to improve social, economic, and environmental issues – flips the often “doom and gloom” narrative of technology by showcasing positive, real-world applications of IoT in our daily lives.

From making our cities smarter and greener to expanding the scope and reach of healthcare in the face of adversity, connected devices are actively being deployed to make a positive global impact, which comprises the heart and soul of the “IoT for Good” movement.

By 2027, the number of households using IoT-connected devices like smart appliances and smart security cameras is expected to more than double; however, the benefits of these devices extend beyond starting your washing machine from the train. Smart devices not only add convenience to our daily lives, but they can help customers lessen their environmental footprint and save money in the process. From washing machines that track water usage, to thermostats that send automatic alerts when usage goals are exceeded, IoT is taking the guess work out of household energy consumption and enabling customers to save valuable time, money, and resources – all while making the planet a bit greener.

“IoT for Good” is also tackling your dirty work, like taking out the trash. Smart waste container management simplifies waste disposal for residents by tracking the frequency and volume of trash disposal. This information is used to enable wireless monitoring to prevent expensive overages.

Effective waste container management is essential for smart cities aiming to modernize and optimize essential services like waste management. It also facilitates wireless fleet tracking, container volume monitoring, and comprehensive management report generation for cities, towns and private businesses alike. IoT enhances waste management services, benefiting residents and city officials by improving efficiency, environmental friendliness, and cost-effectiveness.

In this case study, the value of “IoT for Good’” is highlighted as residents use chip-enabled swipe cards to access waste bins, while sensors monitor the number of bags disposed. This enables municipalities to automatically bill residents for exceeding their allocated limit and prevents misuse of resident bins by businesses. Additionally, sensors issue alerts when containers reach full capacity, aiding officials in monitoring their waste management fleet, thus minimizing overflow and ensuring uninterrupted service. Real-time data collection, monitoring, and analysis in waste management processes not only boost efficiency and cost-effectiveness, but also promote sustainable and eco-friendly waste handling practices.

IoT also promotes health equity by diversifying the ways patients can access healthcare, which is crucial in the face of pandemics and other threats that further strain already taxed hospital systems. Remote Patient Monitoring (RPM) solutions, for example, empower healthcare providers to oversee patients outside of traditional clinical environments, fostering increased care accessibility, cost reduction, and decreased hospital visits. Devices like blood glucose meters, heart rate monitors, and surveillance monitors transmit data via wireless networks to clinicians, enabling hybrid healthcare environments – i.e. virtual wards. These devices utilize IoT connectivity to improve patient outcomes and extend the scope of care provided by clinical staff.

Connected Health (CH) and Telemedicine are crucial companions to RPM, helping to bridge the gap left by the pandemic, our growing aging population, and even conflicts such as the war in Ukraine – all of which are more connected than one might think.

Another case study, GrandPad is a pioneering communication tool tailored for seniors that aims to effortlessly connect them with loved ones and caregivers at the touch of a button. Such devices can also be lifesaving, instantly connecting users to EMS if the need arises. GrandPad enables seniors – as well as those living with disabilities and/or in remote locations – to receive care from the comfort of their own home, avoiding often difficult and costly hospital visits. And in regions devastated by conflict such as Ukraine, where the elderly population has been disproportionately affected, GrandPad bridges the gap by connecting senior refugees with their loved ones.

As divided as the world can often seem, there is one truth that unites us – we’re in this together. Global issues – climate change and pandemics – affect all of us. IoT-powered CH solutions like RPM and telehealth, as well as smart home and waste management solutions, serve the greater good by inching us closer to a sustainable future. IoT-driven sustainability solutions in the private, public, and business sector lay out a blueprint for a brighter tomorrow, wherein technology can be applied to tackle our greatest obstacles. The future is undoubtedly connected, and with the help of initiatives like “IoT for Good,” it can also be one to which we all look forward.

The post “IoT for Good” Paves the Way to a Greener Tomorrow appeared first on IoT Business News.

The combination of generative AI (GenAI) and the Internet of Things (IoT) holds the potential to reshape the future of technology and drive unprecedented innovation.

GenAI promises to revolutionize the IoT ecosystem by enhancing security, personalization, anomaly detection, on-device machine learning, and network management.

As these areas continue to evolve, businesses and individuals are ready to benefit from the innovative applications of GenAI. In this article, we’ll explore the many ways GenAI is beginning to transform the IoT landscape and examine the future possibilities of this powerful alliance.

Creating Synthetic Data for Machine Learning

One of the main challenges in developing machine learning models for IoT devices revolves around collecting and labeling massive amounts of data. However, GenAI solves this problem by creating synthetic data to train these models.

This synthetic data can be used to simulate a variety of scenarios, including machine failures. For example, a manufacturing company can use GenAI to create synthetic data that represents different machine failure scenarios. The company can then use this data to train machine learning models to anticipate potential problems in advance, enabling predictive maintenance.

Crafting Personalized Experiences

GenAI enables IoT devices to deliver personalized experiences to users by leveraging its ability to generate new and original content. Smart home systems can use GenAI algorithms to create personalized lighting and temperature settings for individual users, enhancing comfort and convenience.

And wearable devices can use GenAI to offer tailored workout recommendations based on an individual’s fitness goals and preferences. By leveraging user data from surveys, interactions, and sensor inputs, GenAI can create unique and personalized experiences tailored to the specific needs of each user.

Improving Anomaly Detection

Anomaly detection is key to ensuring the reliability and security of IoT networks. GenAI has the ability to greatly improve anomaly detection by creating synthetic data that accurately simulates normal operating conditions. By training machine learning models on this synthetic data, IoT devices can effectively identify and flag irregular events in real time.

For example, an operator of a power grid can use GenAI to generate synthetic data that mirrors typical power consumption patterns. The power grid operator can then use that data to train a machine learning model that can detect sudden spikes or irregularities in power consumption, enabling the operator to take proactive measures to prevent potential failures or security breaches.

Enabling On-Device Machine Learning

The combination of GenAI and IoT introduces exciting prospects for on-device machine learning. GenAI tackles the issue of limited computing resources in IoT devices by creating smaller and more effective machine learning models.

Anomaly detection models, for instance, can be optimized and implemented directly on IoT devices, enabling real-time analysis and decision-making without depending on cloud resources. This reduces latency as well as strengthens data privacy and security by reducing the need to send data to external servers.

Automating Network Management

Managing large-scale IoT networks requires intelligent automation. GenAI can be instrumental in automating different areas of network management, including configuring devices and optimizing network traffic. With its generative abilities, GenAI can automatically set up new devices as they join the network, simplifying the onboarding process.

Additionally, GenAI can enhance network traffic by intelligently directing data through the most efficient routes, reducing latency and maximizing the use of available bandwidth. This automation lessens the workload of network administrators and boosts the performance and efficiency of the network.

Potential Future IoT Applications

As GenAI continues to evolve, the possibilities for its integration with IoT are endless. Some potential applications include:

Creating New Types of IoT Devices: GenAI can facilitate the development of innovative IoT devices, such as smart assistants with natural language processing capabilities. These devices will be able to understand and respond to human commands and queries, revolutionizing the way we interact with technology.

Enhancing User Interactions: GenAI can enable new ways to interact with IoT devices, including gesture recognition and voice commands. This will make technology more intuitive and accessible, improving the user experience.

Improving Security and Reliability: GenAI can assist in developing advanced security measures for IoT networks, effectively mitigating cyber threats and ensuring data privacy. By generating synthetic data to train anomaly detection models, GenAI can help identify and prevent security breaches in real-time.

Democratizing IoT Access: GenAI has the potential to help bridge the digital divide by making IoT devices more affordable and accessible and allow more people to benefit from the advantages of intelligent connectivity. Democratizing IoT access will allow businesses and individuals to take advantage of IoT technologies for various applications and industries.

Conclusion

The integration of GenAI and IoT has the potential to totally transform how we interact with and benefit from intelligent connectivity. By leveraging GenAI, businesses and individuals can unlock a wide array of applications, ranging from improved anomaly detection and personalized experiences to on-device machine learning and network management automation.

As the field continues to evolve, it’s vital that we embrace the possibilities presented by GenAI and explore its potential to transform IoT. The future of GenAI in IoT is bright, promising a new era of intelligent connectivity and unprecedented opportunities for innovation.

The post Bringing the Power of GenAI to IoT appeared first on IoT Business News.

IoT Analytics published an analysis based on the “IoT Commercialization & Business Model Adoption Report 2024” report highlighting 8 insights from OEMs with business models that are considered more successful.

Key insights:

• Many equipment manufacturers (OEMs) have significantly advanced their IoT strategies, introducing innovative software and services, and revamping their business models. This evolution has enabled some to expand their IoT deployments to millions of devices successfully.
• Connected products are now the norm – It is expected that by 2026 more than 50% of products sold by OEMs will be IoT connected.
• IoT Analytics’ 206-page IoT Commercialization & Business Model Adoption Report 2024 delves into OEMs’ approaches to IoT business models. It highlights key factors that distinguish the more successful OEMs from less successful ones, such as acting on customer equipment usage behavior.

Key quotes:

• Knud Lasse Lueth, CEO at IoT Analytics, remarks: “Our 2024 IoT Commercialization & Business Model Adoption Report, reveals pivotal insights into what differentiates successful IoT implementations among OEMs. A standout finding is the projection that over 50% of products sold by OEMs will be IoT-connected by 2026. The report also highlights the significance of leveraging customer equipment usage data as a cornerstone for innovation, enabling OEMs to offer tailored solutions that significantly enhance customer experiences and operational efficiencies. This report is a clarion call to OEMs everywhere: the path to IoT success is through deep customer insights and innovative business models. It takes years to get there but early innovators show that the journey is worth it.”

• Dimitris Paraskevopoulos, Senior Analyst at IoT Analytics, adds that “From the question of ‘Should I build connected IoT products?’ in 2014 to ‘How should I build my (next) smart connected products?’ in 2024, the shift in OEMs’ approach to IoT is evident. With over 16 billion active connected IoT devices globally, the transformation is not just about sales or specific partnerships. It’s about understanding customer behavior, analyzing it, and better serving their current and future needs.”

How to create a successful IoT business model

Less than 10 years ago, in November 2014, Michael Porter (one of the world’s most influential management thinkers and professor at Harvard Business School) and Jim Heppelman (former CEO at PTC) published a widely recognized article in Harvard Business Review titled “How Smart, Connected Products Are Transforming Competition.” In it, they argued that IoT-connected products would alter traditional industry structures, business models, and the nature of competition in many industries.

While the change has not been as quick as expected, 10 years later, we do have over 16 billion active connected IoT devices globally as of 2023, including consumer devices (e.g., smart homes and watches) and enterprise equipment (e.g., connected factory machinery, electrical equipment, and commercial vehicles). Most large OEMs have a connected product roadmap and, with it, a software and servitization strategy.

In 2014, when the article came out, many OEMs that IoT Analytics spoke with asked, “Should I also build connected IoT products?” This has since dramatically shifted; now, in 2024, the questions OEMs are asking are, “How should I build my (next) smart connected products?” and related to that, “What should the business model look like?”

Prominent examples of OEMs that have innovated their business model and subsequently scaled to hundreds of thousands or even millions of connected devices at this point include:

• BMW, with over 20 million connected vehicles on the road worldwide.
• John Deere, with over 500,000 connected agriculture- and construction-industry machines in the field.
• Schindler, with over 500,000 connected elevators around the world.

Even smaller OEMs are reaching impressive numbers with their connected devices. Take the example of Italy-based professional kitchen machinery manufacturer UNOX, a company with approximately 1,200 employees. Unox started its connected product proof-of-concept stage in 2015 and has since connected more than 30,000 ovens and introduced new revenue streams with it.

There are thousands of other examples of smart product/IoT business models that are scaling to these numbers of connected devices, and the resulting business implications should not be taken lightly—they are often core to the company strategy. Take, for example, US machinery giant Caterpillar, which has set a target of $28 billion in service sales by 2026. The data coming from smart connected IoT trucks, excavators, and wheel loaders play a crucial role in achieving that target.

“Our confidence is increasing that we will achieve our $28 billion services target by 2026. Through tools like the new Cat® Central and SIS2GO apps and insights from data on our more than 1.4 million connected assets, we are creating a superior customer experience as we help customers minimize downtime, improve utilization and extend product life.” – Jim Umpleby, Chairman and CEO at Caterpillar (2022)

In our research for the 206-page IoT Commercialization & Business Model Adoption Report 2024 (published February 2024), we looked at 100 OEMs like Caterpillar to understand: What are the takeaways and best practices these companies have developed as they are scaling their connected products to the thousands or millions?

The data are based on surveys with participants from these OEMs who have knowledge of and can speak to their respective OEM’s IoT business model.

Components of a successful IoT implementation

There are many tradeoffs when bringing a smart connected product to market, for example:

• Which features should we focus on developing?
• Do we monetizethe hardware, the software, a service, or the data? Or perhaps a combination of those?
• Do we charge once, monthly, or perhaps even per usage (pay-per-use)?
• Do we offer some features for free?
• Do we source the tech stack via an external vendor, develop it in-house, or find an open-source solution?

The report provides answers and viewpoints on each of these tradeoffs and highlights which IoT business models are considered to be more successful. This article does not go into the same depth as the report, but it highlights 8 insights that were uncovered during the analysis.

We split our analysis of IoT business models into 4 parts:

1. Making the case for connected equipment (e.g., determining the revenue contribution, outlining key benefits, and highlighting key beneficiaries of connected equipment)
2. Developing the IoT product (e.g., budgeting, sourcing parts, time to market, and developing the features)
3. Developing the business model (e.g., market positioning, key use cases/features, value chain, and revenue model)
4. Commercializing the IoT product (e.g., determining ways to monetize, developing measures to drive adoption)

1. Making the case for connected equipment

Insight 1: 40% of products sold by OEMs are connected.

The survey participants reported that, on average, connected products accounted for 40% of the product mix that was sold in 2023. The participants expect this average to rise to 54% by 2026, though OEMs in APAC are already seeing over 50% of products sold being connected.

Machinery OEMs and electrical equipment makers were the forerunners in this regard as of Q4 2023. However, respondents from OEMs in other major industries are expected to see their connected products take more of the share of the total products sold over the next three years and match those two industries. For example, according to the survey participants, ~34% of products sold by automotive OEMs in 2023 were connected. But by 2026, the participants from that industry expect that the share of connected products for their OEMs will reach 54%, the biggest expected increase among other major industries.

Insight 2: Gaining deep insights into customer usage is the single most valuable feature of connected products.

The research found signs that the core value of connected devices is to drive OEMs and customers closer together. 67% of the survey participants reported that generating deep insights into customer usage of their products and services is either extremely or very useful for their organization—the highest ranked in terms of benefits from connected products. Second to this was better management of customer needs, which 61% of OEMs reported as extremely or very useful.

However, it is not simply about sales or focusing on specific partnerships. Instead, companies find this information more valuable because they can understand customer behavior, analyze it, see how their product is broadly used, and better serve their current and future customers.

“What is happening on a single press might not be valid on a global scale. Machine data helps us to understand what is going on for certain press formats or applications. Since we have all the data from the market now, we do see regional shifts and shifts in applications. That helps us focus our company on what is most important for our customers.” – Thomas Göcke, head of digitalization, König & Bauer

2. Developing the IoT product

Insight 3: OEMs need 41 months to bring their connected products to market.

The research found that the survey participants’ OEMs average 41 months from project kick-off to their first sale (time to market), with 43% of them reporting time to market taking more than 45 months to reach their first sale. Participants in the automotive industry reported the slowest overall time-to-market, with an average of 53 months from project start to the first paying customer. Meanwhile, participants from electrical equipment OEMs reported the fastest, with an average of 33 months.

Insight 4: Microsoft, Cisco, and AWS are the three most mentioned vendors across the tech stack

According to the survey participants, OEMs appear to frequently outsource aspects of their tech stack. 150 unique vendors were mentioned by the 100 OEMs surveyed for this research. The top outsourced parts of the tech stack include connectivity services (e.g., cellular services), connectivity hardware (e.g., modems and gateways), and cloud-based applications.

The most mentioned vendors that the survey participants reported are Microsoft (mentioned in all 12 tech stack categories that we queried), AWS (mentioned in 11 out of the 12 categories), and Cisco (mentioned in 10 out of the 12 categories).

3. Developing the business model

Insight 5: Successful OEMs help their customers optimize workflows.

61% of survey participants from successful companies—those with an amortization time of 24 months or less for their connected product—shared that workflow optimization was crucial or of high value for their customers, while only 21% of less successful companies stated the same—a 40 percentage point gap. This gap, the largest when looking at how successful and less successful OEMs assess the value of the software or service to their customers, reflects that successful OEMs help their customers optimize their workflow.

A notable example of this from the report is German industrial machine manufacturing company Trumpf. Its Oseon software is a workflow optimization tool for sheet metal processors, with features including digital order management, traceability of materials and stock, and optimization of the overall order flow. Trumpf designed Oseon to help improve each step of the sheet metal production process across the workflow of the average sheet metal processor.

Insight 6: Upselling software based on customer usage is the most successful business model innovation.

When it comes to business innovations, OEMs have several options to explore and try, as shown in the preceding table. However, the best-performing innovation, according to the survey participants, is upselling software/services based on actual product usage, where OEMs observe customer product usage and offer relative add-ons. Of the 67 respondents who said their OEM tried this innovation, 60 (or 90%) of them shared that it was successful.

The most tried innovation is offering specific performance guarantees to the customers (e.g., specific uptime guarantees). This also had the second highest success rate at 59%; however, it also comes with more risk, as OEMs must be ready to stand by the promise and be prepared to address issues quickly.

4. Commercializing the IoT product

Insight 7: IT and data security concerns have not left the customers’ minds.

According to the survey participants, on average, the three biggest concerns/roadblocks that customers report when adopting new IoT-based digital services and software are:

• #1: IT/data security concerns
• #2: issues with integrating the product into legacy systems
• #3: lack of budget

Most notable in this statistic is that IT and data security concerns remained the top roadblock since 2020, when IoT Analytics last released a report on IoT business models for OEMs. These concerns are understandable since high-profile security breaches in connected products can remain fresh in the minds of many. For example, in 2021, hackers gained access to over 150,000 cameras produced by US-based building security solutions vendor Verkada, compromising customer data and giving video access to hospitals, jails, schools, and even Tesla cars.

Insight 8: Privacy and regulations are hindering the abilities of OEMs.

Along with customer concerns related to security, regulations aimed at protecting customer data and cyber security standards appear to be hampering European OEMs’ ability to make the most of their connected products. According to the survey respondents, on average, 71% of European OEMs felt that privacy and security laws were limiting their ability to make the most of their connected product solutions. Europe was the only region to increase in this regard from similar research in 2020; North America and Asia decreased by 22% and 18%, respectively, though 56% of North American OEMs expressed feeling similar limitations.

As this sentiment of hindrance by North American OEMs decreased, it is notable that 59% of North American OEMs stated that they own the data generated by their customers, surpassing both European and Asian OEMs by 17 percentage points. That said, 72% of North American OEMs reported that the customer has a say in whether the generated data is shared with the OEM.

Analyst takeaways and outlook

Since Michael Porter and Jim Heppelman’s paper in 2014, most OEMs have developed a business model strategy, finding what works best for them and their products. However, now that OEMs are looking to scale, adapting their existing business models to this growth presents new challenges.

The research in the IoT Commercialization & Business Model Adoption Report 2024 shows many successful commercialization models are scaling, but it shows a fair share of non-successful ones as well. A key question from this is, “Why are some connected IoT product OEMs more successful than others?”

Overall, it appears to come down to OEMs putting the focus squarely on the customer—but behind the scenes, this is more complex than it sounds. While many OEMs claim that they are getting better at putting themselves in their customers’ shoes, there is clearly still room for improvement. The IoT Analytics’ team, for example, struggled to find a good set of OEM webpages that have a clear and well-articulated IoT value proposition that is geared toward real-world customer problems.

Another struggle for OEMs is making the revenue from connected products meaningful. With some equipment costing hundreds of thousands of dollars and the related software available for only a fraction of that cost, many OEMs still struggle to make the business of connected products meaningful enough to the company’s top and bottom line.

One approach to addressing this is the equipment-as-a-service model. Of the various innovative business models in the IoT Commercialization & Business Model Adoption Report 2024, this model is the most innovative and, in turn, the most complex. It is designed around charging for either some or all of the equipment based on usage.

Of the various innovative business models in the IoT Commercialization & Business Model Adoption Report 2024, EaaS is the most innovative and, in turn, the most complex. It is designed around charging for either some or all of the equipment based on usage.

What it means for OEMs

7 key questions that OEM executives should ask themselves based on the insights in this article:

1. Using IoT for customer-centricity: How well do we understand our customers’ usage of our products, and are we leveraging this data to enhance their experience and address their specific needs?

2. Offering workflow optimization: In what ways can our IoT products help customers optimize their workflow, and are we communicating this value effectively in our sales and marketing efforts?

3. Business model innovation: How can we innovate our business model, perhaps through upselling based on customer usage or offering performance guarantees, to enhance profitability and customer satisfaction?

4. Pricing and monetization strategy: What is the most effective pricing strategy for our IoT products? Should we consider a pay-per-use model, subscription-based services, or a combination of different pricing models?

5. Security and privacy concerns: How are we addressing IT and data security concerns in our IoT products, and are we compliant with the latest privacy and cybersecurity regulations, especially in different geographical markets? Can we prove this to our customers and communicate it effectively?

6. Equipment-as-a-Service (EaaS) model: Could the EaaS model be applicable to our products, and how can we structure it to provide clear value propositions and strong customer service?

7. Scaling challenges: As we scale, what are the key challenges we need to prepare for, particularly in terms of adapting our business model and maintaining a customer-focused approach?

The post How to create a successful IoT business model – Insights from successful OEMs appeared first on IoT Business News.

Industry 4.0, also known as the Fourth Industrial Revolution, represents a significant transformation in the world of manufacturing and industry. It is characterized by the integration of digital technologies into industrial processes with the primary aim of improving manufacturing responsiveness, quality, and efficiency. This revolution is reshaping the landscape of manufacturing, enabling companies to achieve higher levels of productivity, flexibility, and self-managing production processes.

In this essay, we will explore the key principles, technologies, and advantages of Industry 4.0, as well as its applicability across various industrial segments.

Principles of Industry 4.0

At the core of Industry 4.0 are several key principles that define its approach to manufacturing and industrial processes. These principles serve as guiding philosophies for the implementation of digital technologies in the industrial sector:

• Interoperability: Interoperability emphasizes the seamless communication and integration of various components within a manufacturing ecosystem. In an Industry 4.0 environment, different machines, sensors, and systems can work together effectively, sharing data and information in real-time. This interconnectedness enables the efficient flow of data and decision-making.
• Virtualization: Virtualization involves the creation of virtual models or digital twins of physical assets and processes. These digital replicas provide a means to simulate and analyze real-world scenarios, allowing for optimization, testing, and troubleshooting without disrupting actual operations. Digital twins are instrumental in predictive maintenance and process improvement.
• Decentralization: Industry 4.0 promotes decentralization by empowering individual components and devices with decision-making capabilities. Rather than relying solely on centralized control, smart machines, and systems have the autonomy to make real-time decisions based on data and predefined rules. This decentralization leads to increased flexibility and adaptability in manufacturing.
• Real-Time Capability: Real-time capability is a fundamental aspect of Industry 4.0, enabling the immediate processing and utilization of data. In a manufacturing setting, real-time data analysis ensures rapid response to changing conditions, such as production anomalies or shifts in customer demand. It supports agile decision-making and optimization.
• Service Orientation: The service-oriented approach in Industry 4.0 extends beyond physical production to include value-added services. Manufacturers can offer customized services alongside their products, creating new revenue streams and enhancing customer experiences. This shift towards servitization is a hallmark of Industry 4.0.
• Modularity: Modularity refers to the design of systems and processes in a way that allows for easy integration, modification, and scalability. Modular systems facilitate the replacement or addition of components without extensive disruption, promoting efficiency and flexibility in manufacturing environments.

Technologies Driving Industry 4.0

Industry 4.0 leverages a range of advanced technologies to bring its principles to life. Some of the key technologies include:

• Cyber-Physical Systems (CPS): At the heart of Industry 4.0, CPS combines physical machinery with digital intelligence. These systems enable real-time monitoring, control, and coordination of physical processes. For instance, a smart factory may employ CPS to optimize production and maintenance processes.
• Internet of Things (IoT): IoT connects devices and sensors to the Internet, facilitating data collection and sharing. In manufacturing, IoT enables predictive maintenance, remote monitoring, and efficient resource utilization. Sensors placed on machinery can transmit data for analysis and decision-making.
• Big Data and Data Analytics: The vast amounts of data generated by IoT devices and other sources require advanced analytics to derive meaningful insights. Big data analytics identifies patterns, anomalies, and opportunities for improvement. Manufacturers can use these insights for quality control, demand forecasting, and process optimization.
• Cloud Computing: Cloud computing provides a scalable and flexible infrastructure for data storage and processing. It supports remote access and collaboration, making it possible for geographically dispersed teams to work together in real-time. Cloud platforms also facilitate the deployment of machine learning models and data sharing.
• Automation and Robotics: Automation in Industry 4.0 involves the use of robots and artificial intelligence (AI) to automate tasks and processes. Robots can handle repetitive and dangerous tasks, while AI algorithms can optimize production, inventory management, and logistics.
• Human-Machine Interaction (HMI): HMI focuses on improving the interaction between humans and machines within the manufacturing environment. Augmented reality (AR) and virtual reality (VR) interfaces enhance operator efficiency and decision-making.
• Additive Manufacturing (3D Printing): Additive manufacturing technologies allow for the creation of complex, customized parts and prototypes. This contributes to the concept of mass customization, where products are tailored to individual customer needs without sacrificing efficiency.
• Blockchain Technology: Blockchain provides a secure and transparent way to record and verify transactions. In supply chain management, it ensures traceability and authenticity of products, reducing the risk of counterfeit goods and enhancing trust among stakeholders.

Advantages of Industry 4.0

The adoption of Industry 4.0 technologies offers numerous advantages to industrial companies, especially amid the challenges presented by events like the COVID-19 pandemic. Here are some of the key benefits:

• Enhanced Productivity: One of the most significant advantages of Industry 4.0 is the substantial increase in productivity and operational efficiency it brings to manufacturing and industrial processes. Through the integration of advanced technologies such as automation, data analytics, and artificial intelligence, production processes become streamlined and optimized. Real-time monitoring, predictive maintenance, and autonomous systems lead to reduced downtime, higher throughput, and improved resource utilization. This enhanced productivity ultimately translates into cost savings and increased competitiveness for businesses.
• Improved Quality Control: Industry 4.0 technologies provide unprecedented capabilities for quality control and assurance. IoT sensors and real-time data analytics enable manufacturers to detect defects and anomalies in products or processes immediately. This allows for timely adjustments, reducing the production of faulty goods and enhancing overall product quality. As a result, companies can maintain higher customer satisfaction levels and reduce costs associated with rework or recalls.
• Flexibility and Adaptability: In a rapidly changing business landscape, flexibility and adaptability are crucial. Industry 4.0 promotes these attributes by decentralizing decision-making and enabling quick responses to market fluctuations and customer demands. Smart manufacturing systems can adjust production schedules, product configurations, and resource allocations in real time. This flexibility not only improves agility but also helps businesses remain competitive in dynamic markets.
• Predictive Maintenance: The implementation of Industry 4.0 allows for predictive maintenance strategies. By continuously monitoring the condition of machinery and equipment through IoT sensors and analyzing data with machine learning algorithms, companies can anticipate when maintenance is needed before equipment failure occurs. This proactive approach minimizes unplanned downtime, reduces maintenance costs, and extends the lifespan of assets.
• Mass Customization: Industry 4.0 enables a shift from mass production to mass customization. Through technologies like additive manufacturing (3D printing) and advanced robotics, companies can efficiently produce personalized products tailored to individual customer preferences. This not only meets the growing demand for personalized goods but also fosters stronger customer engagement and loyalty.
• Digital Operations: The ongoing digital transformation in Industry 4.0 has proven invaluable during unexpected disruptions, such as the COVID-19 pandemic. With remote monitoring and control capabilities, manufacturers can continue operations even when physical presence is limited. This resilience enhances business continuity and minimizes the impact of crises, ensuring that production can continue without compromising safety.
• Sustainability and Resource Efficiency: Industry 4.0 technologies contribute to sustainability efforts by optimizing resource utilization and reducing waste. Predictive analytics and process optimization lead to more energy-efficient operations, reduced material waste, and minimized environmental impact. This not only aligns with corporate social responsibility goals but also reduces operational costs in the long run.
• Competitive Advantage: By embracing Industry 4.0, companies gain a significant competitive advantage. They can deliver higher-quality products, respond faster to market changes, and offer personalized solutions that meet customer demands effectively. This enhanced competitiveness can lead to increased market share, revenue growth, and a stronger market position in their respective industries.

Applicability Across Industries

The transformation brought about by Industry 4.0 is not limited to a particular sector. It is applicable across various industrial segments, including manufacturing, aerospace, food, energy, mining, and healthcare. Let’s explore its applicability in a few key sectors:

• Oil and Gas Industry: The oil and gas sector has adopted Industry 4.0 to enhance exploration, drilling, and production processes. IoT sensors on offshore platforms monitor equipment health and environmental conditions, while predictive maintenance ensures the reliability of critical machinery.
• Mining Industry: Mining companies leverage Industry 4.0 to optimize resource extraction, reduce operational costs, and enhance worker safety. Autonomous mining equipment, equipped with sensors and AI, can operate in hazardous environments, making operations more efficient and less risky.
• Healthcare: In healthcare, Industry 4.0 technologies are used to improve patient care and streamline hospital operations. IoT devices and wearable sensors enable remote patient monitoring, while data analytics support disease diagnosis and treatment planning.
• Additive Manufacturing (3D Printing): Industry 4.0 technologies have revolutionized additive manufacturing processes, allowing for the creation of complex and customized products. 3D printing, supported by digital design and real-time monitoring, enables rapid prototyping, reduced material waste, and on-demand production of parts and products.
• Aerospace and Defense: The aerospace and defense sector uses Industry 4.0 to improve aircraft manufacturing, maintenance, and operations. IoT sensors and data analytics help optimize aircraft performance, reduce fuel consumption, and enhance safety.
• Food and Beverage: Industry 4.0 is used in the food and beverage industry to monitor and control production processes, ensuring food safety and quality. Automated systems and sensors help with inventory management, production scheduling, and traceability.
• Energy and Utilities: The energy and utilities sector employs Industry 4.0 technologies to manage power generation, distribution, and consumption more efficiently. Smart grids, sensors, and real-time data analysis enable better energy management and grid reliability.
• Pharmaceuticals: Pharmaceutical companies utilize Industry 4.0 to improve drug development, manufacturing, and quality control. Automated processes, robotics, and data analytics enhance the production of pharmaceuticals while ensuring compliance with regulatory standards.
• Retail and E-commerce: Retailers and e-commerce companies leverage Industry 4.0 for supply chain optimization, inventory management, and customer personalization. RFID technology, AI-driven demand forecasting, and automated warehouses are some examples of its application.
• Logistics and Transportation: The logistics and transportation industry utilizes Industry 4.0 to optimize routes, track shipments, and improve overall logistics efficiency. IoT-enabled tracking devices, autonomous vehicles, and predictive maintenance play significant roles in this sector.
• Agriculture: Precision agriculture employs Industry 4.0 technologies to enhance crop management, optimize resource usage, and monitor environmental conditions. Sensors, drones, and data analytics assist farmers in making informed decisions to increase yield and sustainability.
• Textiles and Apparel: Textile and apparel manufacturers benefit from Industry 4.0 by automating production processes, reducing waste, and enabling customization. IoT devices and digital twins help monitor and control textile production lines.
• Construction and Real Estate: In construction, Industry 4.0 aids in project management, building design, and maintenance. Building information modeling (BIM) and IoT sensors improve construction efficiency and building performance.
• Financial Services: The financial industry incorporates Industry 4.0 technologies for fraud detection, risk assessment, and customer service. Machine learning algorithms and data analytics are used to analyze financial data and make informed decisions.

Challenges of Industry 4.0 Adoption

While the promise of increased efficiency, productivity, and competitiveness is alluring, the adoption of Industry 4.0 technologies presents several challenges that must be addressed strategically. We will explore the key challenges associated with Industry 4.0 adoption.

• Lack of Internal Alignment: One of the foremost challenges faced by businesses when embracing Industry 4.0 is the lack of internal alignment regarding which strategies to pursue. With the advent of digital technologies, new business models are emerging, necessitating a shift in how companies operate. However, without a consensus on the business strategy, or the right people in place to drive it, internal challenges can impede progress.
• Cybersecurity and Data Privacy Concerns: As businesses become more interconnected through Industry 4.0, there is a heightened concern for cybersecurity and data privacy. The online integration of processes, systems, and people creates vulnerabilities that can be exploited by cyberattacks, potentially resulting in security breaches and data leaks. Companies must make substantial investments in advanced encryption, authentication protocols, and robust cybersecurity measures to safeguard critical information generated by connected devices and systems.
• Workforce Displacement: Automation, a key component of Industry 4.0, can lead to concerns about workforce displacement. As machines and algorithms take on more tasks, the nature of work may change, potentially displacing some workers. This challenge requires companies to address the impact on their employees through reskilling and upskilling initiatives to ensure a smooth transition to new roles and responsibilities.
• Technology Adoption Pathways: The path to Industry 4.0 adoption varies significantly based on the specific technologies being incorporated and the existing infrastructure and skills of organizations. For some, the transition may involve significant changes and investments, while others may find a more gradual approach suitable. Navigating these pathways can be complex and challenging.

Strategies to Overcome Industry 4.0 Challenges

To harness the power of this transformative era, companies must navigate these challenges effectively. Given below are strategies to overcome the adoption challenges.

• Comprehensive Understanding of Capabilities: To address the lack of internal alignment, businesses should start with a comprehensive understanding of their current capabilities. This involves assessing the skills, resources, and technologies already in place. Identifying the gaps that Industry 4.0 can fill is crucial. This assessment may reveal the need for reskilling or upskilling initiatives to ensure that the workforce is prepared for the technological shift.
• Addressing Cybersecurity Concerns: Prioritizing cybersecurity is non-negotiable in the age of Industry 4.0. To mitigate cybersecurity and data privacy concerns, companies must make substantial investments in advanced security measures. This includes implementing robust encryption, multi-factor authentication, intrusion detection systems, and regular security audits. Moreover, fostering a cybersecurity-aware culture within the organization is equally important to ensure that employees are vigilant and informed.
• Change Management Strategies: Effective change management is pivotal in overcoming resistance and driving acceptance of new technologies. Collaborative efforts to manage change within the organization can help address the challenges associated with Industry 4.0 adoption. This involves clear communication of the reasons for the changes, providing training and support to employees, and involving them in the decision-making process where possible. Engaging leadership and leading from the top can play an important role in bringing about the cultural change needed for digital transformation.
• Scalability and Flexibility: Industry 4.0 solutions must be scalable and flexible to adapt to changing demands and future growth. Companies should design solutions that are agile and can evolve with their business needs. It’s advisable to start with smaller, scalable pilot projects that can demonstrate the value of Industry 4.0 technologies before committing to larger-scale implementations. This allows businesses to learn, iterate, and scale gradually.
• End-to-End Approach: Successful implementation of Industry 4.0 technologies requires an end-to-end approach that incorporates people, processes, technologies, and data. Rather than viewing technology adoption in isolation, businesses should consider how it fits into their overall operations and strategy. This holistic approach ensures that technology is integrated seamlessly, and its benefits are maximized.

Conclusion

Industry 4.0 represents a pivotal transformation in the industrial landscape, driven by the seamless integration of digital technologies into manufacturing and industrial processes. Its foundational principles of interoperability, virtualization, decentralization, real-time capability, service orientation, and modularity serve as guiding pillars for the adoption of cutting-edge technologies like Cyber-Physical Systems, the Internet of Things (IoT), extensive data analytics, and automation. Embracing Industry 4.0 yields numerous advantages, including heightened productivity, superior quality control, enhanced flexibility, and the agility to respond to dynamic market conditions.

Moreover, the COVID-19 pandemic has expedited the uptake of Industry 4.0 technologies, as they facilitate digital operations and contactless processes. This transformative shift extends beyond a specific industry; it has wide-ranging applicability across sectors, spanning from discrete manufacturing to healthcare.

In the ongoing progression of the Fourth Industrial Revolution, it becomes imperative for businesses to wholeheartedly adopt Industry 4.0 and leverage its capabilities to maintain competitiveness, efficiency, and adaptability within an ever-evolving global marketplace. The principles and technologies underpinning Industry 4.0 are shaping the future of industry, enabling a more interconnected, efficient, and sustainable approach to manufacturing and production.

Although the challenges associated with Industry 4.0 adoption are substantial, they are by no means insurmountable. Companies that strategically and proactively address these challenges can unlock the full potential of the Fourth Industrial Revolution. By gaining a deep understanding of their own capabilities, prioritizing cybersecurity measures, adeptly managing the process of change, and embracing scalable solutions through a comprehensive approach, organizations can successfully navigate the intricacies of Industry 4.0, positioning themselves for a future that is marked by efficiency, competitiveness, and digital transformation.

The post Industry 4.0: The Fourth Industrial Revolution appeared first on IoT Business News.

“Operators and vendors must prove RedCap’s use cases and drive maturity in the ecosystem to pursue new revenue and monetisation opportunities.”

5G reduced capability (RedCap) is a connectivity standard that enables IoT devices to operate more efficiently and to use less bandwidth than traditional 5G devices (for example, smartphones), which reduces the cost of the associated applications because they do not require full 5G capacity. RedCap technology has the potential to increase the addressable market for 5G while providing additional capabilities over existing low-performance IoT solutions. Both vendors and operators should view 5G RedCap as an opportunity to diversify network use cases and drive network monetisation. However, operators and vendors must also ensure that they consider the strengths and limitations of the technology as part of proving its use cases and driving maturity within the ecosystem.

RedCap technology can offer a functional ‘middle ground’ between high performance 5G and NB-IoT

RedCap technology was first standardised in 3GPP’s Release-17 in 2022. It allows IoT devices to leverage a subset of the capabilities of the 5G network to support applications that fall between the performance requirements of 5G and narrow band IoT (NB-IoT). In this way, 5G RedCap provides a middle ground of connectivity; leveraging some of 5G’s advanced capabilities (such as low latency, high reliability and a higher peak data rates), as well as outperforming 5G enhanced mobile broadband (eMBB) in terms of component costs and battery life (see Figure 1).

Figure 1: Comparison of RedCap’s capabilities with those of enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), LTE-M enhanced machine type communication (eMTC) and NB-IoT

In some scenarios, RedCap could offer a viable alternative to the two existing 4G-based IoT standards: LTE Category (Cat) 1–4 and NB-IoT. The technology will offer the following benefits over existing IoT standards.

• Reduced latency compared with existing LTE-M (eMTC) and NB-IoT technologies. This enables RedCap to support applications that require near real-time data communication.
• Higher peak data rates than LTE Cat 1 and NB-IoT to support new IoT applications that require greater bandwidth.
• Capacity to leverage new 5G capabilities including the benefits of the 5G core, such as network slicing and advanced positioning.

The reduced capabilities of RedCap devices will also offer the following benefits, which are not offered by 5G URLLC or eMBB for IoT devices and applications.

• Improved power efficiency. Compared with eMBB and URLLC, RedCap’s improved power efficiencies create a middle ground in terms of functionality and battery life.
• Reduced costs. As well as potentially lower costs than traditional devices, RedCap devices can use half-duplex frequency division duplex (FDD) transmission mode to leverage less costly switches rather than expensive duplexers.

There are also other new 5G IoT technologies that must be considered by stakeholders, such as mMTC, advanced 5G sensing and passive IoT. However, 5G RedCap offers an alternative to these technologies that is optimal for certain use cases and could create different opportunities in new and existing applications.

RedCap technology will enhance existing applications and enable new use cases, but the ecosystem is not yet mature

The following key applications for RedCap are anticipated.

• Industrial wireless sensors. This includes connected sensors for remote monitoring, predictive maintenance, energy management and asset tracking. While RedCap’s higher cost and power consumption currently limit its suitability in narrowband applications, its benefits in terms of latency and reliability make it suitable for more-critical applications. Its ability to leverage 5G improved positioning and network slicing also expands its addressable market beyond that of 4G IoT.
• Wearables. Small, connected devices (including smart watches, health monitoring devices, fitness trackers and AR/VR headsets) can be worn on the body and equipped with sensors and processors to collect and transmit data. RedCap technology enables wearables to access 5G’s lower latency, higher data rates and advanced positioning, but with a smaller device size, improved power efficiency and reduced costs.
• Surveillance devices. This includes cameras or recording devices, such as security cameras, body cams and facial recognition systems. RedCap technology could offer a lower cost alternative to 5G eMBB connectivity for these applications, potentially improving the business case for 5G connected cameras.
• Smart grids. Smart electrical grids are connected to the network to improve efficiency, reliability and sustainability. The numerous applications of smart grids are dependent on connectivity, such as smart metres, and grid sensors and for real-time monitoring. RedCap is able to bring improved latency, peak data rates and reliability to these applications compared with traditional IoT connections such as NB-IoT and LTE-M.
• Fixed–wireless access (FWA). RedCap technology can lower cost, lower performance and more compact customer premises equipment (CPE) for 5G FWA. This could help FWA to better address some emerging market opportunities where CPE prices are the main barrier to adoption.

Although 5G RedCap will offer a new balance of cellular connectivity for IoT that will be applicable across a range of applications, it remains an emerging technology that needs to prove that it can add value to end users. At the moment, RedCap has still not reached substantial commercialisation and its ecosystem remains immature, which could slow adoption of the technology.

Operators and vendors should see 5G RedCap as a key opportunity, but one that still needs proof and ecosystem support

Although RedCap will not serve all IoT applications, its potential to augment operators’ IoT offerings and to support new market segments makes it a valuable opportunity. Over the past year, trials of RedCap chipsets and products have continued to break ground and its performance enhancements in 5G Advanced are expected to further increase its utility. Consequently, both vendors and operators should see 5G RedCap as an opportunity to diversify network use cases and drive network monetisation. However, investment to support RedCap’s capabilities will only be justified if the industry can prove its advantages and use cases for customers.

Operators and vendors should consider themselves as key enablers of RedCap, not only in terms of connectivity, but also in driving ecosystem maturity. In applications such as surveillance, smart grids, sensors and wearables, both operators and vendors should build specialised partnerships, support new device manufacturers and demonstrate the benefits that RedCap can offer both existing markets and new applications. By doing so, operators and vendors can accelerate the pace of ecosystem maturity to increase RedCap adoption and new revenue opportunities.

The post To maximise the 5G RedCap opportunity, vendors and operators must focus on use cases and ecosystem support appeared first on IoT Business News.

Edge processing is one of the biggest trends in IoT – and for a reason. Processing data close to where it’s generated enables greater speed and volume, while reducing transmission loads. It reduces network latency, boosts scalability and enhances security. It creates the opportunity for AI at the edge to take immediate action – such as automatically preventing a pipeline blowout or keeping a failing generator or pump from tearing itself apart.

Today, IoT applications are making only limited use of edge computing. In most cases, the device at the edge takes whatever data the sensors are sending it and pumps it out over the network. That’s a shame – especially when satellite is the optimal connectivity solution, as it so often is for remote or mobile applications. Wasting satellite bandwidth is never a winning proposition. When a sensor is paired with a satellite-enabled device, it enables smart IoT data management: decision-making at the edge to determine what data is relevant data to send over the network.

Edge data management opens and expands use cases for satellite IoT now and in the future

Four essentials for getting edge processing right

There are four essentials to getting edge processing right in a satellite IoT application: edge technology, AI, the right satellite connectivity and the cloud.

Edge Technology

Edge processing technology needs to strike a balance between two different requirements: providing enough processing power for applications and being inexpensive enough for mass deployment. The solution comes down to smart engineering of devices, from storage and power to sensor connectivity. Many satellite-based and multimode IoT devices are designed to monitor and manage unpowered assets far from electric lines. They need low power consumption, long-life batteries and, in some cases, solar power – and they can benefit from the low cost of today’s multi-megabit flash storage and BLE Low Power technology.

AI at the Edge and Core

In addition to physical design, software engineering can make a substantial difference. On the edge devices, it can put a stop to the “pump it out over the network” approach and, instead, prioritize data and package it efficiently for transmission, saving money on the recurring costs of transmission. The back end of the system is equally important. An efficient, easy-to-use management system for devices, users and business rules keeps the network from streaming unnecessary data and supporting inactive devices and users.

Satellite Connectivity

Satellite has a reputation for being costly, unreliable and, like the famed Starlink network, best used for multi-megabit service. None of that needs to be true. Networks designed for IoT and other small-data applications transmit short, efficient bursts of information, using satellites in low Earth orbit that cover just about any location with a view of the sky. Messages can be sent on a schedule and on AI decision-making at the edge that suits the application.

Cloud

IoT networks, especially serving remote locations, tend to be dynamic, with requirements changing as markets and conditions evolve. Cloud-based applications scale up or down rapidly for applications providing back-end configuration, user and device management, and data translation and analytics.

IoT on the Move

You can see these four essentials at work in the biggest single market vertical for IoT: transportation and logistics.

On any given day, more than 16 million trucks are on the road in North America, including nearly 4 million tractor-trailer big rigs that spend long periods beyond the reach of cellular. There is an average of 2 to 3 unpowered trailers for every one of those big rigs. So, trucking companies spend too much time simply locating trailers in their yard, on the road or at customer locations so they can be matched to trucks. Lack of good information on location causes them to waste money buying or leasing trailers to ensure on-time deliveries.

A low-cost, IoT transmitter on each trailer transforms these businesses. It periodically transmits a GPS location over satellite, along with any sensor data the trucking company wants. Solar-powered, it delivers years of use with little maintenance and has enough processing power to monitor and report on battery level, confirm that it remains attached, and manage data from sensors reporting, for example, whether the trailer door is open or closed. The data transmitted over satellite feeds a cloud-based dashboard that maps the location of each trailer and provides access to sensor data. For one company managing hundreds of trailers, real-time analysis of the GPS coordinates alone showed the company that it did not need 100 trailers it was renting or a new order for 40 more. Total savings exceeded $2 million in the first year.

Making the case for edge processing in satellite IoT comes down to value. It can deliver better latency, greater scalability, reduced transmission costs – but the real value is in the business or operational impact it has for companies on the receiving end of the data. This can far outweigh the cost of the added capability – by as much as the car in your driveway is outweighed by a big rig on the road.

The post Don’t Discount the Edge’s Valuable Role in Satellite IoT appeared first on IoT Business News.

As we kick off 2024, the IoT Analytics team has again evaluated last year’s main IoT developments in the global “Internet of Things” arena.

This article highlights some general observations and our top 10 IoT stories from 2023, a year characterized by multi-decade high interest rates, a challenging macroeconomic environment, and, of course, the advent and excitement of generative AI (gen AI).

General IoT 2023 market

2023 was a year of surprises—both positive and negative. The U.S. and several other Western countries proved highly resilient in the face of higher interest rates and elevated inflation, and they avoided a much-anticipated recession. The 2023 global GDP growth of 3.0% ended up more solid than many had expected at the beginning of the year but still trailed the historic average by 0.8 percentage points.

The Nasdaq Composite, one of the key indices for technology companies, rose 43% in 2023 after dropping 33% in 2022. Not only did investors celebrate the potential peak in interest rates, but they also saw new opportunities with the hype around gen AI. Chipmaker Nvidia (ticker symbol NVDA) gained 246% in 2023, Amazon (AMZN) gained 77%, Microsoft (MSFT) gained 58%, and Alphabet (GOOG) gained 57%—all outshining the Nasdaq.

Against this backdrop, IoT 2023 markets held up steadily, with the number of connected IoT devices growing to approximately 16.7 billion (exact update coming in a few weeks) with roughly $235 billion in IoT enterprise spending (IoT Analytics will publish the 2023 IoT spending later in Q1).

The public relevance of the term “IoT,” which peaked in Q1 2022, continued to see strong interest, holding steady at around 10–20% below the Q1 2022 peak despite the renewed focus on AI (see Google trend graph in the lead image of this article). We did notice, however, that the use of the term “IoT” in corporate earnings calls declined 16% from Q4 2022 to Q4 2023.

With many organizations now managing millions of IoT devices (case in point: in Q4 2023, consumer giant Nestlé announced 2.8 million connected devices through the AWS IoT platform), do not assume that IoT is fading in importance. Quite the opposite: IoT is scaling for many organizations. Our take: IoT is not the “cool” term it used to be. In 2023, companies loved talking about the AI opportunity instead, but at the same time, IoT is quietly scaling.

Top 10 notable IoT 2023 developments

Throughout 2023, we monitored significant developments regarding IoT technology as part of our continued coverage of the field. In our opinion, these are the top 10 notable developments of IoT in 2023 (in chronological order of the leading stories we highlight).

1. Most notable IoT-related regulation: The EU’s NIS2 cybersecurity directive

In January 2023, the EU’s second Network and Information Security Directive (NIS2) became active. This cybersecurity directive comes as a follow-up to the first NIS directive (introduced in 2016) to address shortcomings from the first version, namely inconsistent implementation across member states in terms of what organizations were considered essential.

The new version enforces requirements for cyber risk management and incident reporting across 15 sectors. The intent of NIS2 is to clearly define the organizations meant to comply and to force them to deeply consider their cybersecurity posture, ideally protecting citizens and essential services from cyber threats.

Each EU member state has until October 2024 to adopt laws in compliance with NIS2 by 17 October 2024, giving companies time to look ahead and start compliance without much pressure now. However, as that deadline approaches, companies will begin to feel the compliance pressure, as failure to comply with its measures can cost companies up to €10 million or 2% of their annual global revenue (whichever is higher), as well as possible sanctions and audits.

The specific covered sector in the NIS2 that impacts IoT is digital infrastructure, which covers telecoms, DNS/TLD services, data centers, trust services, and cloud services. The EU projects the annual revenue of this sector to be €85.4 billion and notes that dependence on digital infrastructure opens companies to various cybersecurity risks.

In addition to NIS2, the EU is also expected to start enforcing its Cyber Resilience Act in early 2024. This legislation targets hardware and software products sold within the EU market. Once enforced, manufacturers will have 36 months to start applying the act’s guidelines.

Other notable IoT-related regulations in 2023 included:

2. Most surprising market destabilization: Tech layoffs

In January 2023, Microsoft announced it planned to lay off 10,000 employees between January and March 2023. Most notably for us, the third wave in March saw the largest cut for IoT-, AI-, and supply chain-focused personnel across various levels, functions, teams, and regions.

Microsoft did not have the most tech-industry layoffs—that unfortunate honor appears to belong to Amazon—but it was the most direct hit at IoT and related fields, especially from a company whose IoT services appear to be expanding, at least in the cloud (more on this below).

Google also experienced more layoffs than Microsoft. Though most of its layoffs appeared to be across the board, at times focused on their human resources and recruitment sections, Google shuttered its IoT Core service in August 2023 (also more on this below), meaning roles associated with that service either got moved elsewhere or scrapped altogether.

The fact that three companies seemingly at the forefront of the biggest tech trend in 2023, AI, laid off parts of their team shook the markets and created a lot of uncertainty. The IoT Analytics team noted that some of the laid-off people included high-performers who enjoyed industry-wide recognition, adding to the overall uncertainty of what was happening.

While the thousands of big tech layoffs represented only a small percentage of the respective company employee baseline, it was the startup scene that was most affected by the layoffs. For example, in December 2023, Israel-based grid-computing software startup Incredibuild, known for its accelerated software development technology, including for embedded IoT systems, laid off 40 employees, or 20% of its workforce (75% of which were based in the company’s HQ).

3. Most innovative IoT 2023 connectivity technology development: 5G in space

In July 2023, Spain-based low-Earth orbit (LEO) constellation satellite operator Sateliot and Spanish multinational telecommunications company Telefónica announced the success of their end-to-end test of a roaming 5G cellular network in space. The test process involved an IoT cellular device with a regular SIM card provisioned on Telefónica Tech’s Kite platform—all of this following 3GPP Release 17 non-terrestrial network (NTN) standards and leveraging narrowband IoT (broadly referred to as NB-IoT) communication technology. The device was able to switch between Telefónica’s terrestrial network and Sateliot’s non-terrestrial, low-earth-orbit (LEO) satellite network, demonstrating the integration of both network types using GSMA roaming.

The test, which the European Space Agency supervised, also involved Sateliot’s “Store & Forward” mode, a two-step authentication method developed by Sateliot to store information on a satellite when it is not in a position to connect with a ground station, forwarding the information when it enters coverage range.

IoT solution providers working with sectors that can often experience intermittent connectivity, such as transportation, logistics, or rural agriculture, will see applications to keep their customers connected when between terrestrial 5G network node ranges. Interestingly, in February 2023, Sateliot partnered with space and IoT hardware company GOSPACE LABS to provide 5G NTN NB-IoT connectivity to GOSPACE LABS’ MERATCH water management solution in the US, including water wells in rural areas, and in April 2023, Sateliot applied to the US Federal Communications Commission to bring its space-based 5G NB-IoT technology to the US market.

4. Most accelerated driver of IoT 2023 initiatives: Sustainability and ESG

Amit Kohli, Sr. Solution Director and Sustainability Leader, Orange Business:

“Gone are the [days] of greenwashing. Things are getting more serious in terms of reporting… [It’s a less] casual outlook [than] in the past.”

Europe, and international companies doing business in the EU, witnessed a wave of sustainability directives enter into effect. This is not just one news story but a series of stories that have been on the radar of many for several years.

In January 2023, the Corporate Sustainability Reporting Directive (CSRD) entered into force, enacted as a legal framework that requires all EU companies, except micro-enterprises, to submit annual sustainability reports starting in 2024. Then, on July 31, 2023, the European Commission adopted the first set of European Sustainability Reporting Standards (ESRS), which act as the roadmap for CSRD compliance and require large companies and listed companies to publish regular reports on the social and environmental risks they face. The ESRS became law on 1 January 2024 and now applies directly in all 27 EU member states. Large corporations now must report various IoT-type data, including pollution levels, GHG emissions, and resource use (e.g., water and energy consumption).

Additionally, in November 2023, the EU’s Renewable Energy Directive (Revised Directive EU/2023/2413) became enforceable across all member states. The member states have 18 months to transpose the directive’s provisions into their own national laws, with some provisions having a deadline of July 2024.

With strict adherence guidelines like these, it was no surprise to find increasing emphasis and prioritization on sustainability and energy management at the Smart Production Solutions (SPS) Fair 2023 in Nuremberg, Germany, in November. Coinciding with this, we have noted sustainability and environmental concerns as key topics during CEO earnings calls throughout 2023.

Other notable sustainability ESG regulations in 2023 helping drive IoT initiatives included:

5. Largest IoT-related acquisition: Renesas acquires Sequans

In August 2023, Japan-based semiconductor manufacturer Renesas Electronics agreed to buy Sequans Communications, a France-based cellular IoT chipmaker, for $249 million. The deal, expected to close in early 2024, is poised to expand Renesas’ foray into the IoT sector. The electronics company plans to integrate Sequan’s cellular IoT products into its microcontrollers and other products, enhancing its WAN market reach.

A few months prior, in June 2023, Renesas completed its all-cash acquisition of Panthronics AG, an Austrian-based fabless semiconductor company specializing in wireless products. The deal was originally made in March 2023 for approximately $95 million, and in its announcement of the completed acquisition, Renesas released 13 designs leveraging Panthronics’ NFC technology, showcasing the “embedded processing, power, connectivity, and analog portfolios” of both companies and what customers may be able to look forward to in the near future.

These are just the latest IoT-focused acquisitions by Renesas, but they are by no means the largest by the company financially. The following is a breakdown of its other IoT-oriented acquisitions in recent years:

• 2017: Renesas began its IoT expansion journey by acquiring Intersil, a provider of power management and analog solutions, for approximately $3.2 billion, targeting larger IoT, automotive, and industrial market opportunities.
• 2019: Renesas acquired US-based mixed-signal semiconductor manufacturer Integrated Device Technology, Inc. (IDT) for approximately $6.7 billion.
• 2021: Renesas acquired UK-based Dialog Semiconductor in a nearly $6-billion deal. Dialog had been one of Apple’s major chip suppliers, and this deal sought to expand Renesas’ reach into the IoT, power management, and connectivity solutions market.
• 2022: Renesas acquired Stradian, an India-based manufacturer of 4D imaging radars, for approximately $44 million, aiming to boost its automotive and industrial sensing solution offerings.

Other notable IoT-related acquisition announcements of 2023 included:

6. Most notable software developments: IoT cloud wars

The leading cloud providers, Google, AWS, and Microsoft, all recorded a strong slowdown in cloud revenue growth in 2023 as many organizations started to optimize their cloud spending. AWS, for example, grew by 40% in late 2021 but slowed to 12% growth in late 2023.

On the back of slowing growth, in August 2023, Google made its shock announcement from 2022 a reality and shut down its IoT Core service. The company seemingly now redirects its customers to partners such as Litmus Automation, KORE Wireless, or SoftServe to get the job done (Google’s IoT Core site lists these and other partners on its website to “meet the needs of IoT customers”)

How would Microsoft and AWS react in 2023?

AWS and Microsoft Azure did not follow suit but instead expanded their IoT cloud services in 2023:

Microsoft most notably announced Azure IoT Operations in November 2023, an expansion of its Azure IoT portfolio enabled by Azure Arc. It aims to enable “a cloud to edge data plane with local data processing and analytics to transfer clean, useful data to hyperscale cloud services such as Microsoft Fabric for unified data governance and analytics.”

AWS also announced several IoT extensions to its cloud platform services portfolio in 2023, including AWS IoT FleetWise vision system data and AWS IoT SiteWise Edge on Siemens Industrial Edge B2B marketplace (both in November 2023) as well as a new open-source, no-code IoT dashboard application, aimed at allowing users to visualize and interact with data from its AWS IoT SiteWise service.

7. Largest IoT-related funding round: Pragmatic

UK-based circuits manufacturer Pragmatic Semiconductor raised $389.3 million in 2023. Its latest funding round, Series D, closed on 6 December 2023 and raised the largest venture funding round for a European chipmaker at $206 million.

Pragmatic Semiconductor manufactures flexible, ultra-thin integrated circuits—thinner than a human hair—by leveraging thin-film semiconductors and polymers rather than silicon. The company aims to use the technology to bring intelligence to low-cost items as part of IoT applications, including smart packaging, recycling and reuse, traceability, and product authentication.

Very notable in this latest funding round was that the UK Infrastructure Bank led in investments (alongside M&G Catalyst). While the US and EU worked to establish an early warning system for semiconductor supply chain disruptions and increase investment and trade between the two on this technological front, the UK pursued its own national semiconductor strategy. The strategy aims to support UK leadership in the research, design, and advancement of chip manufacturing, and the UK Infrastructure Bank made the direct equity investment to support this effort.

Other notable IoT-related funding rounds of 2023 included:

8. Best performing IoT 2023 stock: Samsara

After making our IoT 2022 in review list of underperforming IoT company stocks, US-based IoT solutions company Samsara, Inc. (ticker symbol “IOT”), best known for its fleet management and telematics solutions, witnessed 180% growth in its stock in 2023, rising from $11.92 on 3 January to $33.38 on 29 December. Bolstering its climb were three better-than-expected quarterly earnings reports in March, June, and November.

Founded in 2015 by Sanjit Biswas and John Bicket, Samsara specializes in telematics, or “the convergence of telecommunications and information processing,” as it defines it. However, it has expanded its portfolio in the past few years to offer a more holistic connected operations platform and target other industries, such as utilities, manufacturing, and retail.

In 2021, we listed Samsara’s IOT stock as the biggest IoT-related IPO of that year.

9. Most notable IoT 2023 project: 250 million smart meters in India

250 million (or 1.5%) of the current 16.6 billion global IoT devices could soon come from one initiative alone: the ambitious national smart meter roll-out in India. However, though approved in 2021, the project has generally sputtered along, largely due to low domestic production means for the meters while trying to cover a whole subcontinent.

This year, to help spur the project and control the costs, the Indian government opened the projects to a total expenditure (TOTEX) approach, whereby the government can issue $40 billion in grants on completion of the projects and pay per meter. The project’s goal and financial approach have also opened the project to international support. For example, in June 2023, the US International Development Finance Corporation (DFC) announced a formalized $49.5 million investment to India-based smart meter manufacturer Genus Power Infrastructures, aimed at helping the company expand its production of smart meters.

Nonetheless, capacity remained low by the end of 2023, and only 8 million smart meter installations have taken place. As a result, it appears unlikely that India will meet this goal by the end of 2025, but with the spurred investment, the goal could be met not too long after. According to the government, 99 million of the 250 million smart meter contracts had already been rewarded at the end of 2023.

10. Generative AI and IoT breakthrough: None yet

How can recent advances in gen AI, which is mostly text- or image-based, be combined with IoT data, which is mostly based on time-series sensor data? This was one of the top questions we received in 2023.

The answer is … it is complicated.

However, throughout 2023, we have seen several developments on this front, but none have reached scale just yet. Nonetheless, the following are a few initial steps in gen AI and IoT convergence that caught our attention, some of which are discussed in our Generative AI Market Report 2023–2023 (released December 2023).

a) Using national language (gen AI) to query operational data (IoT)

In June 2023, Norway-based industrial software company Cognite launched Cognite AI, a generative AI solution designed for industrial operational data. Built within Gognite Data Fusion, it is meant to enable more tailored, business-specific data retrieval and contextualization in a private, secure manner. Siemens presented a similar example in November 2023, together with Schaeffler, at the SPS fair—which we covered in our SPS 2023 report.

b) Providing guided repair or operations (gen AI) based on operational data (IoT)

In September 2023, Google’s Cloud team shared a video demonstrating their gen AI solution alerting train maintenance operators to potential train issues and proactively providing possible causes and solutions based on manuals and past issue/repair reports.

In November 2023, Microsoft announced Copilot in Microsoft Dynamics 365 Guides, a solution that combines gen AI and mixed reality to assist frontline workers in their tasks. Paired with HoloLens 2 and IoT sensors, Microsoft claims that operators can pinpoint and identify specific assets and access relevant information about them—such as operational conditions or troubleshooting guides—in real time.

c) Using generated images (gen AI) to train vision systems (IoT)

In December 2023, Germany-based engineering and technology company Bosch announced it is piloting gen AI models in manufacturing, whereby they use synthetic, gen AI-created images to develop and scale AI solutions for optical inspection and optimizing existing AI models.

d) Using natural language (gen AI) to teach and control robots with vision systems (IoT)

In February 2023, Microsoft’s Autonomous Systems and Robotics Group released a paper entitled “ChatGPT for Robotics: Design Principles and Model Abilities” (Microsoft is a major backer of ChatGPT’s parent company, OpenAI). In this paper, the research team leveraged ChatGPT’s intuitive language capabilities to control multiple robotic platforms, including arms, drones, and home assist robots. Soon after, in April 2023, Microsoft published another paper, “ChatGPT empowered long-step robot control in various environments: A case application” (last updated in August 2023), in which they demonstrate a specific example of how ChatGPT could be used to convert natural language instructions into robotic actions.

In July 2023, Germany-based AI software and robotics company Sereact announced the release of PickGPT, a gen AI transformer that combines LLMs with computer vision. By combining these capabilities, users can use simple language to instruct a robot to sense conditions or identify objects, offering many potential use cases for remote sensing and control.

Others

We also noted a startup that, in May 2023, announced a revolutionary gen AI solution that would use gen AI to produce synthetical IoT sensor data that could then be used to train AI algorithms. The company has since deleted all references to the announcement.

The post IoT 2023 in review: The 10 most relevant IoT developments of the year appeared first on IoT Business News.

Two investment themes bookended 2023. In January, the European Union backed a $100m venture capital fund, managed by Momenta Partners. In December, Softbank announced its EUR473m ($514m) investment for a 51% stake in Cubic Telecom. This development more than drew the eye as exemplified by the analyst commentary around the high (16x) revenue to implied enterprise value multiple.

In between, the level of corporate activity in the IoT sector continued at roughly the same pace in prior years, albeit down on the years of heightened activity going back five or so years ago. There were several developments among the vendor and network operator communities, but less so among the IoT platform providers. Governments became more active with an emphasis on security and protections for the consumer sector.

Against the backdrop of 5G developments and 6G pathfinding, IoT is becoming a part of the fabric of enterprise operations and national infrastructure. Established players continue to emphasize connectivity, a relatively small portion of IoT value chains, while enterprises focus on quick-to-market solutions enabled by cloud providers and systems integrators. Both approaches risk leaving ‘system of systems’ issues for later consideration.

Investment Splash

As announced, the European Union’s VC funding effort targets Industry 5.0 or, “Industry 4.0 with-a-conscience” prospects because the Industry 4.0 movement is perceived to be overly tech-focused, and one that has failed to prioritise people and the planet. Consumer protection and sustainability are themes that reappear in other developments covered by this review.

SoftBank’s stake in Cubic Telecom drew attention for its high multiples and am implied valuation of over EUR900m for Cubic Telecom. This is a business that raised $124m over a period of some ten years. After beginning life as a company offering an over-the-air software management for M2M applications, it switched to explore the connected car space (working with Tele2) around 2014. An EUR18m investment from Audi and Qualcomm followed in 2015. SoftBank’s investment rationale into Cubic Telecom is to pioneer the future of software-defined connected vehicles. This does not look like an IoT connectivity deal given SoftBank’s 2022 equity investment into 1NCE, the latter being characterised as “the only company that can deliver global IoT”.

One insight on SoftBank’s investment can be gleaned from an even larger IoT investment from several years ago. In 2016, Cisco invested US1.4bn to acquire Jasper Wireless. Compared to Cubic Telecom, Jasper had raised a cumulative investment of $205 million over seven rounds. At the time, Cisco’s acquisition provided it with an entry point into the IoT sector as well as a channel comprising some 3,500 customers including big names such as Ford, GM, Heineken, and Boston Scientific. The acquisition seems to have helped Cisco over subsequent months as it brokered IoT deals with SalesForce.com, IBM and several mobile network operators internationally. Whether SoftBank can achieve the same market gains with Cubic Telecom remains to be seen.

With over 90% of Cubic Telecom’s revenues concentrated in Volkswagen Group, there remains a challenge to diversify the customer base. Of course, SoftBank’s relationships might help with Japanese vehicle manufacturers. This will take time and a greater investment in resources and coalition building. There should also be scope for product and service innovation involving connected car, intelligent transport, and electric vehicle charging systems. It’s worth noting that several months after its Jasper Wireless acquisition, Cisco’s continued foray into the IoT sector led to an additional $3.7bn acquisition of AppDynamics which was active in application performance monitoring, end-user monitoring and infrastructure visibility. Expanding the addressable market might be one factor in SoftBank’s investment calculus.

Incumbents’ Dynamics

Across network operators, connectivity platforms and vendors, the sharpest rise in corporate initiatives points to the ways in which vendors are trying to ease adoption and reduce the friction of developing solutions. For example, ST Microelectronics wants to make it easier to connect devices to cloud providers. It now offers microcontroller software and developer tools targeting Microsoft’s Azure IoT Hub and AWS cloud. ST Microelectronics also partnered with CommScope to integrate the latter’s PKIWorks IoT security platforms to align with align with the Connectivity Standards Alliance’s Matter standard. Making adoption easy applies to another strategic incumbent, Qualcomm. It launched a new platform called Qualcomm Aware comprising Qualcomm silicon and an ecosystem of hardware and software partners all wrapped in a cloud-friendly bundle to simplify the process of “getting into the IoT game”.

Mobile and low-power network operators continued at about the same level of corporate activity as 2022 with two themes apparent. One involved the launch of solutions for distinct verticals. In the utilities sector, for example, Vodafone launched its Water Metering solution for water management companies. Also targeting the water sector, UnaBiz (formerly SigFox) entered into a strategic partnership with KAIFA, a utility sector business digitalisation solution provider. In Australia, Telstra launched an end-to-end industrial automation capability, following its acquisition of industrial IoT providers Aqura Technologies and Alliance Automation. AT&T, one of the forerunners of the IoT industry even decided to relaunch its old “Connected Solutions” business unit. Beginning with connected cars, it wants to help customers navigate the 5G and IoT, by putting dedicated technology and sales executives alongside each other instead of separating them across different AT&T units.

The other theme involved horizontal, or extended connectivity, initiatives. Some of these combined licensed and unlicensed terrestrial network providers (e.g., Bouygues with Netmore Group, UnaBiz with The Things Industries to interwork SigFox and LoRa technologies). Others involved the combination of terrestrial and satellite communications means (e.g., Sateliot with Transatel, Skylo with Telefonica, EchoStar with the Things Industries and, Intelsat with Deutsche Telekom).

Platform providers were less in evidence as far as corporate initiatives are concerned. A marketing report by Analysys Mason for floLive, one of several to publish on eSIM and iSIM developments, suggested industry motivations are driven by a strategy of embedding connectivity earlier in the IoT value chain. For 2023, the requirements associated with this industry change, focused on flexible connectivity, outweighed M&A and platform innovation developments.

Government’s Growing Role

As the sector grows, IoT offerings are starting to expose externalities that purely market-based systems are not geared up to address. That is one explanation for the EU getting involved in VC funding for people and planet issues as noted earlier. In Scotland, the government sees the nation as expanding in a global market valued at $600bn. The country is investing in an innovation hub targeting IoT and related technologies such as sensing and imaging to help Scottish businesses explore opportunities “presented through advanced digital technologies”.

Cybersecurity and consumer protection are other areas where governments can address adverse externalities and set a positive path forward through regulatory and certification measures. For example, the UK government is enacting regulations for Security Requirements for Relevant Connectable Products targeting password management, vulnerability disclosures and software update support. In the USA, the Biden-Harris Administration announced a cybersecurity certification and labelling program via a “U.S. Cyber Trust Mark” to help consumers choose among smart devices that are safer and less vulnerable to cyberattacks. In Asia, the governments of Singapore and South Korea launched an initiative to develop a mutual recognition of IoT security certification schemes. These developments expose market gaps that individual companies and industry alliances are ill-positioned or unwilling to address.

Watching the Horizon

Whether they are labelled opportunities or challenges, other market gaps will shape the IoT industry over 2024 and beyond. Professional media sites such as LinkedIn and Medium are starting to fill up with individuals offering their IoT implementation services, a sure sign that supply and demand are rising up a notch.

Connectivity continues to dominate. To borrow a 1990’s marketing phrase that was commonly applied to sell the commercial Internet, connectivity is analogous to the ‘on-ramp’ for the IoT. However, connectivity represents one of a growing number of elements that contribute to an IoT solution. As the population of connected devices grows it will require both a structured framework and a suite of management services to interoperate at scale. This might emerge as the communications and cloud industries converge on 3GPP planning and a shift in emphasis to massive machine type (mMTC) use cases.

Governments and society are coming to terms that easy access to the Internet results in an asymmetric relationship between users and infrastructure and application providers. As an illustration of the challenges ahead, the Matter protocol set out to make connectivity simple and straightforward for consumers. While homeowners can mix and match devices from a growing ecosystem of suppliers, they still have to choose a home platform for management functions. This element, sitting above the connectivity layer seems to be dysfunctional and not just in a technical sense. As one reviewer put it, “now that Matter is here, these companies are wholly unmotivated to ensure their platforms work well with their direct competitors”. This is an appealing business scenario for system integrators and large platform (or ‘gatekeepers’ in competition law terminology) providers. The supply side of the industry will need to address issues of security, interoperability, certification and possibly data rights now that the wheels of government are rolling.

Another facet of the connectivity discussion is about interworking. How will deployments in large spaces function when they combine wide-area (cellular, satellite) and short-range devices (Bluetooth, Wi-Fi, Zigbee)? Edge computing concepts are applicable so that gateways aggregate short-range connectivity devices, for example. However, there is still a need for additional functionality to provide oversight and management functions and to make these capabilities appealing to developer communities. This dynamic will persist as the number of connected devices grows because many of these will be constrained by factors such as their energy envelope, power budget and sleep-modes of operation. Expect to see an extension of GSMA and TMForum efforts to define APIs that make intelligent management functions accessible to IoT system operators and developers.

A final observation relates to the scope of IoT. Many associate the term with connectivity and connected devices, as if connectivity is the biggest hurdle to overcome. Business users have progressed beyond connectivity and are increasingly adding IoT data management and remote-control capabilities as they deploy solutions for priority or business-critical use cases. Over the longer term, however, users will need to view IoT through a ‘system of systems’ lens. There will be situations that require cross-silo interoperability involving multiple IoT solutions and service providers working together. In addition to business model innovation, the technical challenges associated with improved decision-making will rely on making IoT work with digital twins as well as AI and ML algorithms in a systematic way. Today’s quick and easy solution is to concentrate IoT data in a cloud environment where all processing, intelligence and reporting are centralized. However, quite apart from ceding value to the cloud provider, there will be longer-term requirements for data provenance tracking and causal reasoning that call for bi-directional data flows. The proliferation of constrained IoT devices will call for edge processing and the coordination of distributed information processing and intelligence. These are reasons why notions of IoT connectivity and solutions, in multi-stakeholder settings, need to embrace system of systems approaches.

The post 2023 in Review: Connectivity dominates but IoT-system gaps remain appeared first on IoT Business News.

2023 was an eventful year in the tech sector, where AI went mainstream with the explosion of language learning models.

As we progress into 2024, the integration and evolution of artificial intelligence in various domains are not just changing; they are set to revolutionise the way we approach design, development, and deployment in these sectors.

With advancements in artificial intelligence accelerating at an unprecedented pace, we stand at the cusp of a new era where AI’s influence extends beyond mere automation to become a cornerstone of innovation and efficiency.

The engineering team at ByteSnap Design has been reflecting on the future of AI in the technology and electronics design industries.

Here is the team’s forecast for the pivotal AI trends likely to emerge during 2024 to redefine industry standards and drive forward a new age of technological excellence.

AI to trigger a battle of the Smart Assistants?

AI assistant tools will continue to be integrated into existing tools to make tasks easier. An example of this happening this year is Zoom’s AI companion which can summarise meetings into notes.

Expect to see development tools such as an IDE integration which can generate GIT commit notes, and release notes automatically during 2024.

We also predict companies will be trying to increase their profits with more monetisation from smart assistants such as Alexa and this will drive techy people towards open source alternatives, such as Home Assistant which run locally.

Rise of the Robotaxi

We anticipate that the first un-geofenced electric Robotaxis will become operational and start accepting paying customers over the coming months.

This is likely to scale steadily over the next few years to replace Uber as the transport medium of choice, providing legal issues can be overcome.

Ultimately, this will make car ownership a questionable decision because travelling this way could be cheaper than running a car.

Apple to enter the Generative AI race

We look forward to Apple unveiling a product to join the AI arms race with their own large language model. Other companies are embracing AI faster and already implementing it; for example, Bard into Google Assistant, and Microsoft’s push for AI in their Office 365 products. Nevertheless, Apple have a stronger than most in-house development philosophy, and it’s hard to see them allowing these products to go unchallenged.

Expect announcements in late 2024 from Apple around its generative AI offering.

Further AI disruption for the Smart Home market

Expect to see more innovation in the smart home market as consumers continue to look for ways to reduce their energy bills, with smart thermostats and TRVs becoming ever more popular. Nest are apparently trying to use AI to help understand consumers behaviour around energy consumption and we anticipate that this trend will continue.

With Apple releasing their Pro Vision headset in 2024, we also expect to see some manufacturers trying to compete with a cheaper product. Apple are excellent at design and are sometimes seen as a trend setter, but in this case are quite late to the party With Meta already well-established leaders. However, Apple have a history of knocking out incumbent leaders so this could be an interesting space to watch.

How much of a consumer appetite there is for this type of technology, however, remains open to question.

AI-enabled Integrated Circuits

We’re likely to see the greater emergence of AI on integrated circuits from companies such as Altered Carbon.

Computer chip manufacturers, Intel, for example, are incorporating AI cores into their CPUs.

AI algorithms in our view are mostly used for detection/categorisation. The classic example is using AI to detect whether an image contains a cat or a dog. However, even the way that the likes of Tesla use AI is similar – detecting images of signs for speed limits, or an image of the lines of the road – but the output is different in that it translates it into braking, accelerating or turning.

One of the projects we’ve worked on at ByteSnap sent accelerometer data into the cloud to detect people falling over. We see a scenario where a fall detector algorithm could be generated by AI and embedded within the sensor device, so that the huge amount of data does not need to be sent, allowing the product to consume less power.

Greater AI in Supply Chain Management

AI-powered forecasting is providing businesses with intelligence to prevent mishaps in the future, overcoming demand-supply mismatches to prevent overstock or understock of inventory.
This minimises costs and improves customer experience. We expect to see more of this across 2024. Additionally, AI-based algorithms are automating goods retrieval from warehouses for smooth order fulfilment, and AI-powered autonomous vehicles are reducing driver costs for delivery.

AI in software and electronics design

Software development and electronics design are both areas that AI vendors are targeting, as developers are expensive and timescales can be long. We can see initially the AI could be best at optimising PCB layouts in the hardware side and writing generic functions within software, albeit with dubious copyright infringement.

The work of translating very abstract requirements into real electronics still seems a very long way off though. This is partly due to lack of freely available models to train against in what is a fast-moving industry and little way for the circuits that are available to be assessed.

In addition, electronic engineering is actually quite a person-centred job; dealing with suppliers, customers, manufacturers, colleagues. Software AI trainers have raided github and ChatGPT was able to train linguistic models against the huge wealth of the World Wide Web.

However, for electronics, it will take another generation of AI development before engineering jobs are threatened.

The post ByteSnap Electronic Industry Predictions for 2024 appeared first on IoT Business News.

IoT Analytics published an analysis based on the “Generative AI Market Report 2023–2030” report and highlights the landscape with its top players in the data center GPU, foundational model and platform, and generative AI services markets.

Key insights:

• The generative AI market went from nearly nothing to a hot market within a year, as shown by IoT Analytics’ latest research report.
• IoT Analytics analyzed 3 interconnected markets for generative AI: 1) data center GPUs, 2) foundational models and platforms, and 3) generative AI services. Each has distinct aspects and market players.
• NVIDIA leads the data center GPU segment with a 92% market share, while OpenAI and Microsoft have a combined share of 69% in the foundational models and platforms market. The services market is more fragmented, with Accenture currently seen as the leader with a 6% market share.

Key quotes:

Knud Lasse Lueth, CEO at IoT Analytics, remarks: “The speed of generative AI innovation with new offerings coming on the market on a weekly basis is fascinating monitor. Nvidia with 92% market share for data center GPUs as well as Microsoft and OpenAI with a combined 69% market share in the models and platforms segment are firmly in the lead in their respective market segments. With hyperscalers developing their own data center chips, with the availability of powerful open-source models and with giants like AWS and Google looking to differentiate with their new offerings, it will be interesting to watch how much the early lead is worth for the current market leaders. I personally do expect both Microsoft and Nvidia to maintain their strong positions in the coming years but the gap to the competition will likely close a bit.”

Philipp Wegner, Principal Analyst at IoT Analytics, adds that:

“The Generative AI market is rapidly evolving, with established leaders and a growing number of startups. In 2024, it’s a make-or-break year for Gen AI vendors, as they navigate a crowded field of competitors.”

The leading generative AI companies

The rise of generative AI

Following its release of ChatGPT in 2022, OpenAI experienced an impressive one-year, zero-to-$1 billion revenue bump—surpassed only by US-based chipmaker NVIDIA, which managed to increase its data center GPU sales from $3.6 billion in Q4 2022 to an expected $16 billion in Q4 2023. When it comes to generative AI companies, these two stand out.

The generative AI foundational models and platforms market is expected to reach nearly 5% of global software spending by 2030

According to IoT Analytics’ Generative AI Market Report 2023–2030 (published December 2023), the generative AI software and services market reached $6.2 billion in 2023. Although it is still very early to forecast where things are going from here, the IoT Analytics research team expects the generative AI foundational models and platforms market to make up nearly 5% of global software spending by 2030 due to its disruptive nature and tremendous value potential.

However, this does not include the market for individual generative AI solutions. The team believes generative AI will become standard within most software in the near future. This also does not include the hardware market, such as for data center GPUs, since this market is looked at separately from software but is discussed below.

In this article, we dive into the data center GPU, generative AI foundational model and platform, and generative AI services markets, discussing what aspects of the generative AI field make up each market and highlighting the leading generative AI companies within them.

Market segment 1: Data center GPU market

a.) Market overview

The data center GPUs market refers to specialized GPUs designed to handle the extensive computation demands of modern data centers, which are the backbone of generative AI. Originally designed for rendering graphics, GPUs excel at parallel processing, which is fundamental for deep learning computations used in generative AI.

The report shows the data center GPUs market reached $49 billion in 2023—a booming increase from 2022 (+182%), mostly driven by one company alone: NVIDIA. Although the market for data center GPUs has seen steep price increases and is undergoing severe supply constraints, there is currently no reason to believe demand will decline in the next two years.

b.) Leading data center GPU companies

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

The data center GPU market at this point has one very clear leader. However, the market report shows that there are other promising startups and other established companies trying to make inroads.

1. NVIDIA

NVIDIA leads the data center GPU market by a long shot, owning 92% of the market share. In 2023, the company’s quarterly revenue jumped 272%, from $4.3 billion in Q1 to a forecasted $16 billion in Q4.

The NVIDIA A100 Tensor Core GPU is the de facto standard for data center GPUs. However, as discussed in the report, hardware is not the only differentiator for NVIDIA. Some consider their developer ecosystem, CUDA, as NVIDIA’s biggest moat, and it is often cited as the key reason why NVIDIA is not set to lose its dominant position anytime soon.

2. AMD

The Data Center segment of US-based semiconductor AMD player, NVIDIA’s first real GPU challenger, grew by 21% from Q2 2023 to Q3 2023 and shared 3% of the market. However, AMD has big ambitions in 2024 to eat into NVIDIA’s market share. In early December 2023, it announced the release of its Instinct MI300 Series accelerators, which are cheaper than NVIDIA’s comparable accelerators and, as AMD claims, faster. AMD’s CEO, Dr. Lisa Su, forecasted at least $1 billion in revenue in 2024 through this chip alone, and Microsoft, Meta, and OpenAI stated they would use the Instinct MI300X in their data centers. AMD also recently launched ROCm 6.0 to provide developers with an ecosystem that is equally attractive to CUDA.

3. Intel and others

US-based chipmaker Intel, the traditional competitor to NVIDIA and AMD, has lagged behind on the data center GPU front. In May 2022, Intel’s Habana Labs released its second generation of AI processors, Gaudi 2, for training and inferencing. Though not as fast as NVIDIA’s popular H100 GPU, it is considered a viable alternative when considering price to performance.

Meanwhile, in July 2023, startup chipmaker Cerebras announced it had built its first of nine AI supercomputers in an effort to provide alternatives to systems using NVIDIA technology. Cerebras built the system, Condor Galaxy 1, in partnership with the UAE, which has invested in AI research in recent years.

Market segment 2: Generative AI foundational models and platforms market

a) Market overview

The foundational models and platforms market comprises two related areas. Foundational models are large-scale, pre-trained models that can be adapted to various tasks without the need for training from scratch, such as language processing, image recognition, and decision-making algorithms.

Generative AI platforms, in turn, refer to software that enables the management of generative AI-related activities outside of foundational models. Notably, IoT Analytics identified six platform types: 1) development, 2) data management/databases, 3) AI IaaS/GPU as-a-service, 4) middleware & integration, 5) MLOps, and 6) user interface and experience (UI/UX).

The foundational models and platforms market exploded with the public release of ChatGPT in late 2022, reaching $3.0 billion in 2023. This is substantial growth over 2022, which saw next to nil in terms of revenue. IoT Analytics’ analysis projects strong market growth in the coming years as enterprises invest billions in—and report real value from—generative AI implementations and continuous improvements.

b.) Leading generative AI foundational model and platform companies

Unsurprisingly, the foundational model and platform market are currently led by OpenAI, with several well-known technology companies trying to catch up.

1. OpenAI

With the November 2022 launch and subsequent success of ChatGPT, OpenAI leads in the share of the foundational model and platform vendors market with 39%. Since the release of ChatGPT, OpenAI’s generative pre-trained transformer (GPT) models went from GPT-3.5 to GPT-4 to GPT-4 Turbo, showcasing the continued development of the model. OpenAI’s models continue to impress in independent model assessments and rankings—often coming out in the top three of all tested models. Although many experts expect the foundational model space to become a commodity over time, at this point, OpenAI’s flagship models remain the top foundational model on the most common benchmarks.

According to IoT Analytics’ What CEOs Talked About series, in 2023, ChatGPT skyrocketed in boardroom discussions in Q1, but as other foundational models and generative AI applications became available, mentions of ChatGPT steadily declined as “generative AI” separated and continued to rise. (The What CEOs Talked About in Q4 2023 report and blog is expected to be released mid-December 2023.)

2. Microsoft

On OpenAI’s heels at 30% market share is Microsoft, its largest shareholder. Microsoft’s platform, Azure AI, offers Azure OpenAI, which uses OpenAI’s LLMs but goes beyond the public ChatGPT offering by promising greater data security and custom AI apps. This is suited for enterprises who want to secure their proprietary data when leveraging the benefits of generative AI since ChatGPT’s terms of use state that they can store and use content (both input and output) to improve their services. In November 2023, Microsoft reported over 20,000 active paying customers for its Azure AI platform, adding that 85% of Fortune 100 companies used it in the past year.

Despite Microsoft’s strong partnership with OpenAI, Microsoft also heavily promotes the usage of other models, such as Llama 2, via its platform, thereby enabling customers to freely choose and test different models and providers. Another key priority for Microsoft is integrating AI capabilities into its existing product portfolio, such as Azure, Microsoft/Office 365, and Bing.

3. AWS

AWS has an 8% share of this market. Its Bedrock service, publicly released in September 2023, provides access to models from several AI companies, such as Anthropic, AI21 labs, and Cohere (each with a 2% share of this market), and combines them with developer toolsets to help customers build and scale generative AI applications.

AWS has quickly claimed the third spot in this market because the company is the market leader in public cloud services and quickly got its existing customer base excited about its differentiated approach to Generative AI. In contrast to Google and Microsoft, AWS Bedrock focuses on providing a platform service that gives users access to a number of both general and domain-specific foundational models from a variety of vendors—providing choice, flexibility, and independence.

4. Google

In 2022, most experts credited Google as being the one tech company at the forefront of AI. Many experts interviewed by the IoT Analytics team continuously praised Google for its AI and its data products and innovations. In 2023, the picture is different, and Google is fighting to defend its position as an AI leader.

Vertex AI is Google Cloud’splatform focused on machine learning (ML) ops. It is integrated with other Google Cloud services, such as BigQuery and Dataproc, and offers a Jupyter-based environment for ML tasks. In early December 2023, Google released a preview version of its new multi-modal flagship model, Gemini. The related technical report states that the largest of the Gemini family outperformed other existing models in 30 out of 32 common ML benchmarks. Initially, the announcement of Gemini was widely received as positive, but a popular demo video released by Google later turned out to be staged.

Market segment 3: Generative AI services market

a) Market overview

The generative AI services market represents a specialized segment dedicated to consulting, integration, and implementation support for organizations aiming to integrate generative AI capabilities. With generative AI having risen as one of the top discussion points in boardrooms, services companies are sensing a large opportunity in helping companies formulate their generative AI strategies (e.g., what use cases to implement), advising them on technical architecture choices (e.g., which models to use) and helping them implement and build individual solutions.

IoT Analytics assesses that the generative AI services sector’s opportunity is now. Due to the novelty of generative AI, organizations often lack skills and experience, and the only option is to look for professional services firms that have or are in the process of building up the required expertise.

b) Leading generative AI services companies

The generative AI services market is more dispersed than the other two markets highlighted here.

1. Accenture

Accenture is estimated to have the largest generative AI services market share at 6%. The company announced in June 2023 that it is investing $3 billion in data and AI practice over three years to double its AI talent and develop new capabilities. Additionally, Accenture disclosed in its Q4 2023 earnings press release that its revenue for generative AI projects grew to $300 million for 2023.

In November 2023, Accenture announced plans to launch a network of generative AI studios in North America where companies can explore ways to integrate generative AI applications. These studies are expected to be at Accenture Innovation Hubs in Chicago, Houston, New York, San Francisco, Toronto, and Washington, DC.

2. IBM

US-based technology corporation IBM makes up 2% of this market. To position itself for the opportunities that generative AI brings, the company announced it had established a “Center of Excellence (CoE) for generative AI,” which as of May 2023, already had over 1,000 consultants specialized in generative AI. The CoE operates alongside IBM’s AI and Automation practice, which includes over 21,000 data and AI consultants.

3. Capgemini

France-based IT services company Capgemini also has a 2% share in this market, offering consulting services intended to help clients adopt key technologies such as the cloud and AI. In July 2023, Capgemini announced the launch of a portfolio of generative AI services, including in the following areas:

• Strategy
• Customer experience
• Software engineering
• Custom solutions for enterprise

One of Capgemini’s current customers is London Heathrow Airport which aims to improve traveler experiences through its “Generative AI for Customer Experience” offer. Heathrow’s Director of Marketing and Digital, Pete Burns, stated that the project is intended to “assist, empower and delight passengers” with tailored customer service solutions.

4. The many others

Past this point, the remaining 86% of the market becomes a cornucopia of specialized generative AI services providers and larger general consulting and system integration companies, each taking a bite of the rapidly growing segment.

As an example, in April 2023, UK-based professional services company PwC announced plans to invest $1 billion over three years to not only grow its AI offerings but also transform how it works by using generative AI. Additionally, in July 2023, global consultancy firm McKinsey & Company announced it partnered with AI startup Cohere to provide customized AI solutions to its enterprise clients.

Generative AI company landscape outlook

The enterprise generative AI market is roughly a year old, and already, the generative AI companies landscape appears vast.

IoT Analytics released its first generative AI report, the Generative AI Trend Report, in March 2023. Since then, more foundational models and platforms have emerged, e.g., OpenAI’s GPT4 Turbo, Google’s Gemini, or Microsoft’s Phi-2. At the same time, the demand for data center GPUs exploded, which is also mirrored in NVIDIA’s stock performance (+231% year-to-date as of 14 December 2023). Finally, consulting giants have made investments to position themselves in the generative AI services market, such as Accenture’s $3B investment in AI and its pledge to double “AI talent.”

As part of this research, we talked to 30+ experts in the field and gathered information on 270+ generative AI projects and analyzed which industries and departments are fastest to adopt and which vendors are most selected today.
The coming months will reveal how many of those projects will deliver value besides just being a marketing coup or how many of those currently in the proof-of-concept stage will move forward. Most companies are only now forging their generative AI strategies and considering whether to build foundational models from scratch based on industry-specific data, use an out-of-the-box propriety model, or fine-tune open-source models. All of this comes as generative AI companies release new products at unprecedented speed.

There is still a lot of movement in the generative AI company landscape, and there will be more in the foreseeable future. IoT Analytics will stay on top of this space, with a follow-up report expected in 2024.

The post The leading generative AI companies appeared first on IoT Business News.

The global smart city movement represents a major shift in how urban environments are designed, experienced and navigated. This monumental change is driven in part by digital transformation and Internet of Things (IoT) technologies, which are reshaping urban infrastructure and cityscapes into hubs of intelligent connectivity. Central to this trend is the emergence of advanced wireless technologies that align with the unique demands of smart cities. Among these emerging technologies, Wi-Fi CERTIFIED HaLow^TM stands out as an ideal wireless protocol for smart city connectivity.

Wi-Fi HaLow, an evolution of conventional Wi-Fi, is purpose-built to serve the needs of IoT applications. Incorporating the IEEE 802.11ah standard, it was released as a new certification by the Wi-Fi Alliance in November 2021. Wi-Fi HaLow operates in the sub-GHz band and surpasses traditional Wi-Fi in the 2.4, 5 and 6 GHz bands in terms of range, coverage and power efficiency, redefining the boundaries of wireless connectivity for smart city and IoT applications. Wi-Fi HaLow has the capacity to connect more than 8,000 devices from a single access point, providing long range connectivity beyond 1 km, low power consumption, advanced Wi-Fi CERTIFIED WPA3^TM security, and massive network density – precisely the attributes demanded by smart cities.

Building on the strengths of the IEEE 802.11ah standard, Morse Micro is developing next-generation Wi-Fi HaLow solutions that extend 10 times farther and cover 100x the area of traditional Wi-Fi networks. These advancements further the goals of smart city application developers, facilitating long-range connectivity, automating urban services and promoting environmental sustainability.

A prime example of this innovation is the potential impact of Wi-Fi HaLow networks on smart city transit systems. Traditional network infrastructure upgrades often reach bottlenecks due to the high cost and complexity of expanding wireline networks, underscoring the need for new forms of long range wireless technology. In such scenarios, Wi-Fi HaLow’s superior range, penetration and performance offer a transformative solution, far surpassing the range limitations of conventional Wi-Fi in the 2.4, 5 and 6 GHz bands while outperforming the low data rates of low-power wide-area networks (LPWANs) such as LoRa.

Wi-Fi HaLow’s versatility enables it to combine diverse building automation systems into a unified connectivity platform that provides an optimal balance of speed and range, and allows innovative IoT applications that may combine video with sensors, for example. It provides seamless connectivity between real-time operational data and the people and systems managing smart buildings, data centers, industrial processes, and other urban utilities. Its extended range and advanced security make it ideal for connecting a plethora of subsystems, from HVAC and smart lighting to microgrids and edge AI cameras.

By using standards-based Wi-Fi HaLow, the total cost to deploy and manage network services for smart cities is lower than other wireless solutions. Wi-Fi HaLow uses license-free radio spectrum in its operation, and Wi-Fi HaLow enabled equipment can be sourced from multiple OEMs. Unlike cellular service providers, which charge fees to use their networks, there is no recurring cost to use Wi-Fi HaLow connectivity. Expert personnel who understand Wi-Fi technology are plentiful and can use well-established methodologies to operate and maintain Wi-Fi HaLow networks. These economic benefits help reduce smart city operating costs, and the savings can trickle down to a municipality’s citizens.

On an enterprise level, Wi-Fi HaLow supercharges a wide array of smart city applications including security and safety systems, energy management, maintenance, occupant services, utility billing, demand management, indoor air quality (IAQ) monitors and compliance systems. With its distinct advantages in range, power efficiency, network capacity and security, Wi-Fi HaLow can equip these applications with the capacity to handle amounts of IoT device connectivity, significantly enhancing operations and services within a smart city.

Wi-Fi HaLow’s unique blend of long range, low-power consumption, advanced security and high-density connectivity is transforming smart city applications. Whether in support of automated transit systems, streamlined building operations or enhanced enterprise applications, Wi-Fi HaLow is a powerhouse protocol capable of addressing the myriad needs of a smart city. Its ability to connect thousands of IoT devices across sprawling urban landscapes enables efficient data sharing and automation, driving improved city services, environmental sustainability, and a higher quality of life for residents.

As cities worldwide transition to smart, connected environments, advanced wireless protocols like Wi-Fi HaLow have become key enablers of technology innovation. By providing a connectivity solution tailored to the distinct requirements of IoT applications, Wi-Fi HaLow isn’t merely contributing to the development of smart cities – it’s setting a higher standard of wireless communications. Wi-Fi HaLow’s growing market momentum represents a significant leap toward a smarter, safer, and more connected future, reshaping our urban landscapes one city at a time.

The post Wi-Fi HaLow: Powering the Evolution of Smart Cities appeared first on IoT Business News.

The Internet of Things (IoT) landscape in 2024 is set for transformative changes, driven by advancements in cybersecurity, artificial intelligence (AI), and a plethora of emerging technologies, as IoT systems become increasingly integrated into critical infrastructure.

In this article, I shall delve into the various aspects of this transformation, exploring the impact of AI and machine learning (ML) in creating intelligent IoT systems, the rise of edge computing, the integration of blockchain for enhanced security, the introduction of ultra-thin smart shipping labels, the incorporation of the SGP.32 standard, and IoT’s burgeoning role in sustainability.

Increased focus on IoT cybersecurity

In 2024, the integration of IoT devices into vital systems like Smart Cities, coupled with the increased adoption of technologies such as 5G, eSIM, iSIM, and satellite connectivity, has emphasised the importance of robust cybersecurity measures. These advancements have made IoT devices more versatile and efficient, but they also necessitate enhanced focus on safeguarding data integrity and device security.

To address these needs, there’s a growing emphasis on deploying advanced encryption and rigorous security protocols. These measures ensure the protection of data transmitted between IoT devices and central systems. Additionally, continuous monitoring and real-time threat detection, powered by AI and ML, may well become standard practices. They help in promptly identifying and responding to potential security breaches, maintaining the integrity and reliability of IoT networks.

AI and ML enabling intelligent IoT systems

AI and ML are revolutionising almost everything, including IoT. By analysing massive amounts of data instantaneously, AI enhances IoT applications such as predictive maintenance and energy management. This synergy, combined with centralised IoT management platforms, leads to unparalleled operational efficiency.

In 2024, the integration of AI and ML will become much more embedded in IoT infrastructures. The blend of AI’s analytical capabilities with IoT’s data collection and monitoring functions creates an ecosystem where operational insights are gathered more efficiently and effectively, leading to smarter, more responsive IoT systems.

Edge computing enhancing IoT performance

Edge computing is revolutionising IoT performance by processing data closer to its source. This method significantly reduces latency, crucial for real-time applications such as autonomous vehicles, industrial automation, and augmented reality. These advancements are particularly pertinent in smart cities, healthcare, manufacturing, and retail, where they facilitate immediate data analysis and improve service quality.

Looking forward, the integration of AI and machine learning with edge computing is expected to increase, enabling edge devices to independently make complex decisions. The expansion of 5G networks will enhance communication between these devices, promoting faster, more efficient data processing. Furthermore, edge computing’s role in reducing energy consumption and carbon emissions underscores its significance in fostering a more sustainable IoT ecosystem.

Blockchain for IoT security

As IoT devices increasingly handle sensitive data, the role of blockchain in bolstering IoT security is becoming more prominent. Blockchain’s decentralised nature offers enhanced data integrity, making it a key player in protecting against the growing cybersecurity threats in the IoT landscape. Its integration with AI and ML is particularly noteworthy, representing a significant leap forward in building a resilient IoT infrastructure.

This combination promises to shape a stronger, more secure IoT ecosystem for 2024 and beyond, especially as the attack surface of IoT expands. Blockchain’s ability to ensure the authenticity and security of data transactions across the network is vital in this context, presenting a robust solution to the evolving challenges in IoT security.

Ultra-thin, low-power smart shipping labels

The ultra-thin, low-power smart shipping labels, first seen in early 2023 with our very own Smart Shipping Label, which is equipped with a printed, eco-friendly battery, features an eSIM, and supports up to 1000 messages across LTE-M, NB-IoT, and 2G networks.

Such labels will become much more prolific in 2024, due to their function as advanced tracking devices for items both large and small. They are capable of real-time monitoring of location, temperature, and package integrity, ensuring secure and efficient transit.

Thanks to their adaptability for various logistical needs, from tracking small documents to larger assets, these smart labels not only enhance supply chain efficiency but also align with sustainability goals, representing a significant advancement in IoT-driven asset management.

Integrating SGP.32 into the IoT ecosystem

The integration of the SGP.32 standard into the IoT ecosystem in 2024 heralds a significant advancement in device capabilities and application efficiency. SGP.32 is pivotal for use cases that demand high location accuracy, like precision agriculture, by providing superior geolocation services.

Moreover, the incorporation of SGP.32 plays a key role in the expanded use of eSIMs within IoT devices. This is particularly beneficial for global IoT deployments, as it simplifies the complexities associated with device management across different regions. Features like remote provisioning and profile swapping inherent in eSIM technology are instrumental in enhancing operational efficiency.

This development is not just a technological leap; it’s a strategic enabler for more efficient, globally connected, and responsive IoT ecosystems. The impact of integrating SGP.32 will be felt across various sectors, significantly contributing to the overall evolution and effectiveness of IoT applications.

IoT’s sustainability drive intensifies

Finally, in 2024 IoT will continue playing its pivotal role in driving sustainability across various sectors. Advanced, energy-efficient sensors, coupled with AI, are revolutionising resource management by enabling precise monitoring and control. This technological synergy is significantly reducing waste and optimising energy use.

In industries like manufacturing, IoT adoption is being accelerated by tightening global regulations, which are mandating more sustainable practices and better ecological footprints. IoT technologies are not only enhancing operational efficiencies but also promoting environmental stewardship. The implementation of smart systems in areas such as energy management and waste reduction are evidence of IoT’s growing influence in creating a more sustainable future.

As the world grapples with environmental challenges, the integration of IoT in sustainability efforts is becoming increasingly crucial, marking a new era where technology and ecology harmoniously intersect.

The post 2024 IoT evolution: Cybersecurity, AI, and emerging technologies transforming the industry appeared first on IoT Business News.

In today’s fast-paced industrial and manufacturing sectors, safety and efficiency are paramount concerns. Companies are increasingly turning to innovative technologies to transform their workplace culture, with the Wearable Internet of Things (WIoT) taking center stage. This cutting-edge technology is not only overhauling traditional practices but also revolutionizing the way companies approach worker safety and productivity. In this article, we will delve into the ways wearable technology is currently reshaping the industrial and manufacturing landscape, explore the myriad benefits of WIoT, and shed light on the software solutions that are propelling this revolution.

A Shift in Workplace Culture

Industrial and manufacturing environments have long been associated with rigorous physical demands and safety concerns. However, as technology advances, so too does the ability to safeguard workers and improve overall efficiency. Wearable technology, in particular, has emerged as a game-changer. From smart helmets to augmented reality glasses, these devices are revolutionizing the way workers interact with their environment.

One of the most significant shifts brought about by WIoT is the move towards a more proactive approach to safety. Traditionally, safety measures were often reactive, focusing on addressing incidents after they occurred. With wearable devices, companies now have access to real-time data that enables them to identify potential hazards before they become accidents. For example, smart vests equipped with sensors can monitor environmental conditions, such as temperature and air quality, alerting workers and management to unsafe conditions instantly.

The Multifaceted Benefits of WIoT

The adoption of WIoT is not solely driven by safety concerns; it also promises a host of other benefits. One of the most compelling advantages is its ability to reduce worker fatigue. In physically demanding industries, fatigue can lead to accidents and decreased productivity. WIoT devices can monitor a worker’s biometrics, such as heart rate and body temperature, in real-time. When fatigue is detected, alerts can be sent to both the worker and their supervisor, prompting necessary breaks or adjustments to tasks.

Furthermore, WIoT is facilitating the digital transformation of facilities. These devices are no longer just tools for monitoring workers; they are becoming integral components of interconnected systems that optimize operations. For instance, by equipping machinery with IoT sensors, it becomes possible to track equipment performance, anticipate maintenance needs, and reduce downtime. This seamless integration of WIoT technology results in cost savings and improved efficiency.

Enhancing Body Mechanics with Wearable Devices

The realm of body mechanics in the workplace is also being revolutionized by WIoT. Wearable devices, such as exoskeletons and wearable sensors, are designed to support workers and help them maintain correct postures and motions. These devices are equipped with sensors that can provide real-time feedback to workers, guiding them to adopt ergonomic positions that reduce the risk of musculoskeletal injuries.

Additionally, the data and understanding collected by these wearable devices is a goldmine of information. To harness this potential, companies are turning to sophisticated software solutions. These solutions aggregate data from various wearable devices and integrate it into a centralized platform. This allows for comprehensive analysis and insights that were previously unattainable.

For example, advanced analytics can identify patterns of movement and posture that may lead to injuries over time. By utilizing this data, companies can implement targeted training programs to improve worker ergonomics and reduce the risk of chronic injuries. Furthermore, the data can be used to engineer workflows, optimize the allocation of tasks and resources for maximum efficiency.

The Power of Integration

Integration is key to unlocking the full potential of WIoT. By consolidating data from wearable devices into a single platform, companies can achieve a holistic view of their operations. This data-driven approach enables predictive maintenance, real-time safety monitoring, and workflow optimization, all within one cohesive system.

Moreover, the benefits of WIoT extend beyond the factory floor. Office-based employees can also benefit from wearable technology, as it can monitor posture and sedentary behavior, promoting better health and well-being. For instance, smart wristbands can remind office workers to take breaks, stretch, or adjust their sitting positions, reducing the risk of long-term health issues.

Embracing Innovation: WIoT’s Role in Shaping Tomorrow’s Workplace

The Wearable Internet of Things is ushering in a new era of workplace culture, where safety, efficiency, and worker well-being take center stage. Companies that embrace WIoT are not only reducing the risk of injuries but also driving digital transformation, reducing worker fatigue, and optimizing operations. With the integration of advanced software solutions, the potential for improvement is boundless.

As more companies recognize the transformative power of WIoT, it is clear that this technology is here to stay. It is no longer a matter of if, but when, organizations will adopt WIoT to enhance worker safety and productivity. The future of industrial and manufacturing workplaces is being shaped by wearable technology, and those who embrace it are poised to lead the way in the evolving landscape of worker safety and efficiency.

The post Enhancing Worker Safety and Efficiency: The Wearable Internet of Things (WIoT) Revolution appeared first on IoT Business News.

In the face of escalating climate challenges, technology has emerged as a beacon of hope. The Internet of Things (IoT) stands out as a particularly powerful tool in the global effort to promote environmental sustainability. With its network of interconnected devices and sensors, IoT offers innovative solutions to monitor, understand, and address environmental issues, contributing significantly to the fight against climate change.

IoT: A Game-Changer for Climate Monitoring

Climate change is a complex beast, with a multitude of variables that must be tracked and analyzed. IoT technologies offer unprecedented granularity in environmental monitoring, with sensors capable of providing real-time data on everything from atmospheric CO2 levels to the health of ocean ecosystems. This data is invaluable for researchers and policymakers alike, offering up-to-the-minute insights that can inform responsive and effective environmental policy.

Energizing Renewables with IoT

Renewable energy sources like solar and wind power are crucial in the transition away from fossil fuels. IoT is instrumental in optimizing the performance of these energy sources. Smart sensors can track wind patterns and sunlight exposure, adjusting the positioning of turbines and solar panels to maximize energy capture. Moreover, IoT systems help in predicting maintenance needs, reducing downtime, and enhancing the overall efficiency of renewable energy infrastructures.

Smart Agriculture: Growing More with Less

Agriculture consumes a vast amount of our planet’s resources, but IoT is helping to change that. Precision farming techniques, underpinned by IoT, enable farmers to monitor soil moisture levels and crop health with pinpoint accuracy, leading to more judicious use of water and pesticides. This not only helps in conserving precious resources but also results in higher yields and better-quality produce.

Waste Not: IoT for Waste Reduction

Waste management is another area where IoT shines. Smart waste bins can signal when they are full, optimizing collection routes and frequencies. IoT systems also play a crucial role in the recycling industry, where they can sort materials more efficiently and identify contaminants that can hinder the recycling process.

The Smart Grid: An IoT-Enabled Energy Network

One of the most significant applications of IoT in sustainability is the development of smart grids. These intelligent energy distribution networks can balance supply and demand in real time, reduce energy wastage, and integrate a higher percentage of renewable energy sources. Consumers can play an active role in energy conservation through smart meters that provide real-time feedback on energy consumption, encouraging more responsible usage patterns.

Challenges to Overcome

Despite its vast potential, the widespread adoption of IoT for environmental sustainability is not without challenges. The energy consumption of IoT devices themselves is a concern; thus, it is imperative that these devices are designed to be as energy-efficient as possible. Additionally, the production of IoT devices must become greener, employing sustainable materials and minimizing waste.

Data privacy and security are also critical issues. The vast amounts of data collected by IoT devices must be kept secure to protect against breaches that could undermine public trust in these technologies.

Policy Implications and the Path Forward

To fully harness the potential of IoT for environmental sustainability, collaborative efforts are needed. Policymakers must create frameworks that encourage the development and deployment of sustainable IoT solutions. This includes investing in infrastructure, funding research and development, and setting industry standards that prioritize sustainability.

Cross-sector partnerships are equally important. The technology sector must work with environmental scientists, urban planners, and agricultural experts to create IoT solutions that are both technologically advanced and environmentally sound.

Conclusion

IoT offers a powerful arsenal of tools in the fight against climate change, from optimizing renewable energy to enabling smarter agriculture and waste management. However, the journey to a sustainable future requires more than just technology; it demands a collective commitment to innovation, responsible usage, and global cooperation. As we continue to harness the potential of IoT, we move closer to a more sustainable world where technology and the environment exist in harmony, combating climate change one smart solution at a time.

The post Harnessing the Power of IoT for Environmental Sustainability: Smart Solutions to Combat Climate Change appeared first on IoT Business News.

The Global AI Safety Summit 2023, held at Bletchley Park and chaired by the UK, was a ground-breaking event that brought together 150 global leaders from various sectors to discuss the future of Artificial Intelligence (AI).

The agreement on the Bletchley Declaration marked the Summit, emphasising collaborative action for AI safety and the need for a shared understanding of AI risks and opportunities. A significant development was the initiation of the State of the Science Report, led by Turing Award-winning scientist Yoshua Bengio, aimed at providing a science-based perspective on the risks and capabilities of frontier AI.

During the Summit, there was a strong focus on the necessity of state-led testing of AI models, and the importance of setting international safety standards was highlighted. The UK’s announcement of launching the world’s first AI Safety Institute underlined its commitment to leading in AI safety research and testing. Summit participants also recognised the need to address current and future AI risks, emphasising standardisation and interoperability to mitigate these risks effectively.

While the majority of current conversations surrounding the impact of AI remain broad and high-level, it’s crucial to acknowledge the significant influence it will have in the realm of IoT. As we delve into this specific area, it is evident that not only will AI play a pivotal role in shaping IoT’s evolution, but the reverse is also true.

The data generated from IoT applications will not only feed into AI systems, enhancing their capabilities, but also emphasise the importance of data integrity. This mutual influence underscores a dynamic relationship where both IoT and AI will significantly shape each other’s development, making it imperative to recognise and address the intertwined futures of these technologies.

In fact, the evolution of AI’s capabilities in processing the vast data generated by IoT devices is propelling a transition from reactive to proactive and predictive operations across various sectors. This paradigm shift is not only about efficiency and reliability but also about establishing trusted and authentic data sources, which is where the Identity of Things (IDoT) comes into play.

Moving from basic identifiers to unique digital identities, IDoT ensures the authenticity of data and strengthens the trust in IoT ecosystems. Implementing technologies such as embedded SIM (eSIM) and integrated SIM (iSIM) is instrumental in this process. They enable better security through robust access control, enhanced data integrity, and reduced vulnerabilities while also addressing privacy concerns.

By ensuring compliance with regulatory standards, eSIM and iSIM contribute to standardisation and reliability, which are critical for scalable and interoperable IoT networks. These technologies support personalisation and accountability, leading to enhanced traceability and the capacity for more advanced predictive analytics.

As AI and IoT continue to converge, the focus on unique digital identities through IDoT will become a cornerstone in achieving a secure, reliable, and adaptable technological ecosystem, ready for the future of interconnected devices.

However, a critical aspect of integrating AI with IoT is ensuring the data integrity of the inputs. The data sourced for AI processing must be not only authentic and secure but also trustworthy. This is because the decisions made by AI are only as reliable as the data upon which they are based. Any security vulnerabilities at the point of data collection or transmission could lead to significant, potentially catastrophic, consequences.

It is, therefore, essential for multi-party IoT ecosystems to establish and maintain data integrity to prevent such risks. Technologies such as SIGNiT by G+D are addressing this critical need by employing digital signing of data generated by IoT devices, coupled with blockchain technology, to create a secure and trustworthy data environment. Ensuring the fidelity of data at its source is fundamental to building AI systems that can be trusted to make sound decisions.

The path forward is fraught with challenges, particularly concerning data privacy, AI’s decision-making transparency, and the reliability of AI algorithms. A significant concern is ensuring that AI integration does not inadvertently create vulnerabilities within IoT systems. To significantly mitigate these risks, we can harness advanced cryptographic techniques.

For instance, elliptic curve cryptography (ECC) is one such technique that provides high levels of security with smaller key sizes, making it more efficient for IoT devices which often have limited computational power. By incorporating blockchain technology and employing advanced cryptography like ECC, we can establish robust security protocols to protect data integrity and maintain the trustworthiness of AI-driven IoT systems.

In essence, the integrity of the entire data stream can be maintained by securing data right at the source – the IoT sensor – and using private keys on secure elements like SIM cards. However, integrating AI into existing IoT systems presents issues beyond data integrity alone. Such integration is a complex endeavour that demands a multifaceted and sophisticated approach to tackle various technical and operational challenges.

On the technical front, it involves ensuring compatibility between AI algorithms and diverse IoT devices, managing the vast data streams generated by these devices, and maintaining the responsiveness and reliability of the systems. The integration must be seamless, ensuring that AI algorithms can effectively interpret and act on the data from IoT devices without causing system lags or errors.

Moreover, this integration significantly impacts business models and operational workflows. For businesses, incorporating AI into IoT systems often means rethinking how they collect, analyse, and utilise data for decision-making. It requires shifting from traditional business processes to a more dynamic, data-driven approach.

Operationally, there’s a need for continuous monitoring and maintenance of these integrated systems, ensuring they operate efficiently and effectively. This shift also necessitates training and upskilling of staff to manage and leverage these advanced systems.

The overarching goal is to ensure that AI acts as a catalyst for enhancing IoT functionalities, not as a barrier. It should streamline operations, provide deeper insights, and open new avenues for innovation and efficiency rather than complicate or hinder existing processes. Thus, integrating AI into IoT systems is not just a technological upgrade but a transformative process that reshapes how organisations operate and interact with technology.

The successful implementation of this integration hinges on a careful balance – leveraging the advanced capabilities of AI to enhance IoT functionalities while also adapting to the new challenges and opportunities this fusion presents, with a clear and necessary focus on data integrity.

As we stand at the cusp of a technological revolution with AI and IoT at its core, balancing the immense opportunities with the inherent challenges is imperative. Ensuring data integrity, securing IoT ecosystems, and maintaining a controlled integration of AI are essential steps towards harnessing the full potential of these technologies.

The AI Safety Summit is just the beginning of a critical journey. The real challenge lies ahead in our industry’s hands. In the IoT sector, we must actively drive the development of responsible and effective strategies for AI integration. While the Summit set the stage, it’s now our responsibility to act.

The post AI and IoT: Post-AI Summit reflections on safe integration and data integrity appeared first on IoT Business News.

As the Internet of Things (IoT) continues to expand, interoperability within IoT ecosystems has emerged as a critical issue. With an ever-growing number of IoT devices and platforms, ensuring these systems can effectively communicate and work together is paramount. This article delves into the challenges of interoperability in IoT ecosystems and discusses strategies to overcome these hurdles, ensuring seamless integration and functionality.

Understanding Interoperability in IoT

IoT ecosystems are rapidly evolving, encompassing a wide array of devices from home appliances to industrial sensors. As of 2023, the number of connected IoT devices globally is in the billions, a number that is expected to grow exponentially. This growth, while promising, introduces complexity and challenges in maintaining interoperability among diverse systems.

Interoperability in IoT refers to the ability of different IoT systems and devices to communicate, exchange, and interpret shared data with one another, regardless of the manufacturer, model, or operating system. This is vital for creating efficient, scalable, and sustainable IoT ecosystems.

Challenges in Achieving Interoperability

• Diverse Hardware and Standards: IoT devices are produced by numerous manufacturers with different hardware configurations and standards, making interoperability a significant challenge.

• Varied Communication Protocols: IoT devices use a range of communication protocols (like Wi-Fi, Bluetooth, Zigbee, and others), which often lack uniformity, further complicating interoperability.

• Data Format and Semantic Differences: Even when devices can connect, differences in data formats and semantics can hinder effective communication and data exchange.

• Security Concerns: Ensuring secure data exchange between devices while maintaining interoperability is a complex challenge, given the varying security protocols and standards.

Strategies for Ensuring Interoperability

• Adopting Universal Standards and Protocols: Developing and adopting universal standards and protocols is crucial. This includes efforts by organizations like the IEEE, IETF, and ISO to create and promote widely accepted standards.

• Open Platforms and APIs: Encouraging the use of open platforms and Application Programming Interfaces (APIs) allows different devices and systems to communicate more seamlessly.

• Modular Design and Frameworks: Implementing modular designs in IoT devices can facilitate interoperability, as it allows for easier integration of components from different manufacturers.

• Common Data Models and Semantic Frameworks: Establishing common data models and semantic frameworks ensures that data exchanged between devices is understood consistently across different systems.

The Role of Industry Consortia

Industry consortia play a significant role in driving interoperability in IoT. Organizations like the Open Connectivity Foundation (OCF), the Industrial Internet Consortium (IIC), and the Zigbee Alliance work towards creating unified standards and certification programs for IoT devices and systems.

Government and Regulatory Bodies

Government and regulatory bodies are increasingly involved in setting guidelines and regulations to promote interoperability in IoT. This includes setting compliance standards for security and data privacy, as well as encouraging the adoption of universal standards.

The Importance of Testing and Certification

Testing and certification are crucial for ensuring interoperability. This involves rigorous testing of IoT devices and systems to ensure they can operate seamlessly across different ecosystems and comply with established standards.

Case Studies: Success Stories of Interoperability

Several industries have successfully implemented interoperable IoT ecosystems:

• Smart Home Technology: Companies like Apple, Google, and Amazon are working towards interoperable smart home ecosystems, allowing different smart home devices to communicate regardless of the brand.

• Healthcare: Interoperable IoT systems in healthcare have enabled better data sharing across various medical devices, improving patient care and operational efficiency.

• Manufacturing: In the manufacturing sector, interoperable IoT systems have streamlined production processes, allowing different machines and sensors to work in unison.

Future Trends and Developments

Looking ahead, the trend is towards increased standardization and interoperability in IoT. This includes the development of more sophisticated AI and machine learning algorithms to manage and facilitate interoperability across complex IoT ecosystems.

Conclusion

Interoperability remains a key challenge in the expanding world of IoT. However, through collaborative efforts, the adoption of universal standards, and the implementation of robust testing and certification processes, significant strides are being made. As we advance, the focus on interoperability will continue to grow, playing a critical role in the success and sustainability of IoT ecosystems.

The post Interoperability in IoT Ecosystems: Navigating Challenges and Strategies appeared first on IoT Business News.