IoT Analytics released a research article that highlights 6 out of 17 industry trends included in the Embedded World 2024 Event Report.

This report presents key highlights and in-depth insights assembled by the IoT Analytics analyst team from one of the world’s leading fairs for the embedded community.

Key Insights:

• The current state of embedded systems was on full display at Embedded World 2024, with a clear emphasis on edge AI.
• As part of the Embedded World 2024 Event Report, IoT Analytics’ team of four on-the-ground analysts identified 17 industry trends related to IoT chipsets and edge computing—this article highlights 6 of these trends related to edge AI.

Key Quotes:

Satyajit Sinha, Principal Analyst at IoT Analytics, remarks:

“The shift towards edge AI will necessitate that CPU vendors develop not only high-performance multi-core CPUs but also integrate specialized NPUs into their SoC designs. The recent increase in demand for NVIDIA GPUs—driven by AI workloads—and the prevailing AI chip shortages have led to upward pressure on prices within the AI chipset market and could continue to do so for the foreseeable future.”

About Embedded World 2024

Embedded World is a leading event for the embedded systems community. This year, it took place from April 9 to April 11 in Nürnberg, Germany, and once again, it showcased the latest developments and innovations in embedded systems, embedded software, chipsets, edge computing, and related topics.

Attendance was up 19% from the previous year and has returned to pre-pandemic participation levels (~32,000 visitors). The number of vendors, too, returned to and even surpassed pre-pandemic levels, with a record 1,100.

IoT Analytics had a team of four analysts on the ground. They visited more than 60 booths and conducted over 35 individual interviews to comprehensively understand the most recent developments in embedded systems, with a special focus on IoT.

Embedded World 2024 emphasized the integration of AI within embedded systems, with a clear focus on edge AI. Corporate research subscribers can refer to the 67-page Embedded World 2024 Event Report for more information about the event, including highlights from keynote speeches, important announcements and launches, and major trends identified by the team. Here, the team shares only six of these trends, each based on observations about the future of edge AI.

6 edge AI trends observed at Embedded World 2024

“Edge AI will reshape our world in a profound way.”

Edge AI was the key theme throughout the conference. Salil Raje, SVP of adaptive and embedded computing at AMD, best captured the energy around this topic during his keynote address, stating, “We stand on the brink of an era where edge AI will reshape our world in a profound way.”

On the stage, Salil Raje and Eiji Shibata, CDO at carmaker Subaru, discussed how AMD and Subaru are collaborating on an edge AI system for autonomous driving based only on cameras—with the vision to achieve zero accidents by 2030.

Below, the team highlights 6 trends it observed on the topic of edge AI.

1. NVIDIA becoming a key edge (AI) computing company

US-based chipmaker NVIDIA has played a crucial role in driving the adoption and implementation of AI technologies across various sectors. NVIDIA’s GPUs, renowned for their high-performance capabilities, specifically in data centers, are also becoming integral to deploying complex AI models at the edge. With a partner network of over 1,100 companies, NVIDIA has established a dominant position in the AI technology market, far ahead of its competitors AMD and Intel.

At Embedded World 2024, one such partner, Taiwan-based embedded systems provider Aetina, introduced its AI-driven industrial edge solutions powered by NVIDIA GPUs, such as its AIB-MX13/23, which is powered by NVIDIA’s Jetson AGX Orin GPU capable of 275 trillion or tera operations per second (TOPS). Using a portable ultrasonic testing device connected to the AIB-MX13/23, Aetina and its partner, Finland-based defect recognition solutions provider TrueFlaw, demonstrated a non-destructive evaluation method for fault detection.

Additionally, Taiwan-based fabless semiconductor company MediaTek showcased four new embedded systems-on-chips (SoCs) for automotive applications—CX-1, CY-1, CM-1, and CV-1—which support NVIDIA’s DRIVE OS 3 autonomous vehicle reference operating system. This application demonstrates how NVIDIA’s technologies are expanding into new domains beyond the gaming and data center GPUs they are generally known for.

2. Simplifying on-device AI inferencing processes for developers

The integration of on-device AI comes with various challenges. One key challenge that developers often face is the dilemma of investing in new devices before they can evaluate the performance of the AI chipset and its compatibility with an AI model. Evaluation factors for developers can include device TOPS, CPU/NPU percent utilization, and temperature. To solve this and other related problems, companies are launching new AI developer platforms that can simulate on-device AI performance, allowing developers to test AI model deployment using specific edge device/chipset resource specifications without purchasing the physical hardware.

One solution on display at Embedded World 2024 was Taiwan-based IoT and embedded solutions provider Advantech‘s EdgeAI SDK platform. This platform supports deploying AI models over widely recognized AI chipsets like Intel, NVIDIA, Qualcomm, and Hailo. Advantech showcased a pose detection model running on an AIMB-278 industrial motherboard integrated with Intel’s ARC A380E embedded systems GPU. Advantech’s EdgeAI SDK facilitated the model’s deployment.

3. AI model training shifting to the thick edge

AI model training is shifting from centralized cloud setups to thick-edge locations like servers or micro data centers. This is possible due to the integration of high-performance CPUs and GPUs that enable powerful computing at the edge, AI training, and multiple AI inferencing capabilities. Further, AI training can also happen on vendor premises, reducing reliance on cloud infrastructure, lowering costs, enhancing privacy, and improving the responsiveness of AI applications on edge devices.

Just before Embedded World 2024, US-based computer builder MAINGEAR and Taiwan-based memory controller manufacturer Phison announced the launch of MAINGEAR PRO AI workstations integrated with 4x NVIDIA’s RTX 5000 Ada or 4x RTX 6000 Ada GPUs with more than 1000 TFLOPS computing power.

At the event, Aetina launched its AIP-FR68 Edge AI Training platform, supporting various 4x NVIDIA GPUs with up to 200 teraflops—the number of float-point operations a chip can perform—of computing power, a lot for a single GPU.

4. Accelerating micro- and thin-edge AI through NPU integration

Integrating dedicated NPUs within edge devices greatly enhances AI inference capabilities. Additionally, it results in power savings, improved thermal management, and efficient multitasking, enabling the deployment of AI in power-sensitive and latency-critical applications, such as wearables and sensor nodes.

At the fair, the Netherlands-based semiconductor manufacturer NXP showcased its new MCX N Series MCUs, which provide 42 times faster ML inference than CPU cores alone. Additionally, UK-based semiconductor design company ARM demonstrated an ARM Cortex A55-only setup and an ARM Cortex A55 + ARM Ethos U65 NPU setup for AI inferencing. The latter setup offloaded 70% of AI inferencing from the CPU to the NPU, with an 11x improvement in inference performance.

5. Localizing autonomous decision-making via cellular-connected micro- and thin-edge AI

Integrating AI-enabled chipsets directly into cellular IoT devices is on the rise, marking a transformation toward intelligent, autonomous IoT systems capable of localized decision-making. This trend will likely substantially impact industries like smart cities and factories, and it brings significant advantages, including real-time data processing, reduced latency, and greater efficiency due to smaller form factors.

An example is the intelligent mowing robot solution displayed by China-based wireless communications module vendor Fibocom. It utilizes a Qualcomm-based intelligent module for powerful on-device computation, allowing it to not only map its environment and avoid obstacles but also perform cost-effective boundary recognition, all without constant reliance on the cloud. This practical application demonstrates the tangible value of AI-enabled chipsets in IoT devices.

Further, the US-based IoT solutions joint venture Thundercomm showcased its EB3G2 IoT edge gateway, which leverages a Qualcomm SoC for on-device AI model execution. This SoC enables immediate data analysis, reducing latency and cloud dependence. The gateway’s algorithms are capable of human detection and tracking, making it valuable for security and traffic management.

6. Tiny AI/ML bringing micro-edge AI capability to traditional devices

As the name suggests, tiny AI/ML are small-sized AI and ML models capable of running on resource-constrained devices, such as sensor-based micro-edge devices. The analyst team noted several cases of tiny ML being integrated into everyday objects and tools, enabling them to perform decision-making functions autonomously without the need for cloud connectivity. This approach bolsters privacy and data security by processing information directly on the device—at the very edge.

UK-based voice intelligence platform developer MY VOICE AI showcased NANOVOICE TM, a speaker verification solution powered by tiny ML and designed for ultra-low-power edge AI platforms. The solution combines passcode verification with speaker recognition for enhanced security.

Likewise, US-based AI/ML software company SensiML demonstrated a proof-of-concept for a smart drill that uses AI/ML models to classify different screw fastening states. The model is capable of both real-time edge sensing and anomaly detection. Further, Norway-based fabless semiconductor company Nordic Semiconductor showcased its Thingy53 IoT prototyping device embedded with Nordic’s nRF5340 chipset, which enables anomaly detection via embedded ML. When paired with an accelerometer, the Thingy:53 senses equipment vibrations using an embedded tiny ML model. As an example, this system could cut off power to a device or machine when it detects anomalies.

The future of the embedded world: what these edge AI trends mean for IoT embedded systems

Embedded World 2024 emphasized the growing role of edge AI within IoT systems. The developments the team witnessed focused on easier AI inferencing and a spectrum of edge AI solutions (micro, thin, and thick), pointing to greater intelligence at network edges.

Edge AI is shifting intelligent computation away from cloud-centric models and moving it closer to data sources. Driving this shift are reduced network traffic, near-instantaneous decision-making for time-critical applications (e.g., manufacturing, autonomous systems), and enhanced privacy by processing data locally. Ultimately, edge AI reduces reliance on hyperscalers and promotes broader AI usage outside centralized infrastructure. It holds transformative potential across healthcare, automotive, and robotics, with the capability to reshape operational paradigms within these industries.

Looking ahead, edge AI will have varying impacts across edge levels:

• Thick edge AI: Facilitate the execution of multiple AI inference models on edge servers or at the network periphery and support AI model training or retraining for scenarios involving sensitive data on premises
• Thin edge AI: Enhance the intelligence of existing sensors and devices by utilizing gateways, IPCs, and PLCs for AI processing at the network edge
• Micro edge: Enable direct AI integration into sensors, improve the scalability of intelligent systems, and empower everyday connected devices to make autonomous decisions

The post The top 6 edge AI trends – as showcased at Embedded World 2024 appeared first on IoT Business News.

Fibocom, in collaboration with Qualcomm Technologies Inc., is proud to announce the launch of their cutting-edge Linux-based edge AI solutions which integrates a series of Qualcomm Technologies-powered Fibocom smart modules.

This new series of solutions utilize a wide array of Fibocom’s smart modules SC171, SC171L, SC138, and SC126 series that are developed based on the Qualcomm® QCM6490/QCS6490, Qualcomm® QCM5430/QCS5430, Qualcomm® QCM6125, and Qualcomm® QCM2290 processors from Qualcomm Technologies with integrated Linux system, will unleash the peak performance of Linux-based industrial applications with robust connectivity and scalable operating system compatibility, and also accelerate the integration of advanced 5G, edge AI across emerging industries like robotics. With the expansion of digitalized industries, Fibocom will intensify the Linux-focused embedded computing intelligence, and leverage expertise in edge AI inclusion to help industry customers realize responsive decision-making, real-time communication and ultra-reliable system interoperability in industrial automation, smart manufacturing, smart retailing through the partnership.

At the “heart” of Industry 4.0, interoperability is crucial for operators to get insights into the equipment’s performance in the field. This groundbreaking series solutions is poised to advance the industrial applications that demand wireless connection, high-integration expertise with design-in Linux operating system for easy integration, and long-term product lifecycle optimization. By adopting Linux-supported Fibocom’s smart modules, Linux engineers around the world have the ability to develop industrial devices such as industrial PCs (IPCs), edge AI workstations, smart POS terminals, and industrial routers with higher efficiency as well as potent multimedia processing capabilities.

Harnessing the interoperability of the Linux operating system, industry-focused customers catered to the benefits below:

• Utilizing the stability and reliability of the Linux system, the Linux-based edge AI solutions integrated with Qualcomm Technologies-powered smart modules ease the concerns of diverse system integration to industrial control systems and enable the 24/7 data transmission and management of industrial equipment with minimum risk of malfunction and downtime.
• The Linux-based Edge AI Solutions are highly flexible and customizable compatible with industrial standards, and can be widely deployed in industrial PCs (IPCs), industrial cameras, edge AI workstations, smart POS terminals, and robotics.
• Inherently beneficial from the robust feature and open-source nature of the Linux operating system, the solutions allow equipment manufacturers to safeguard product development and management with long-term upgradable support through the entire lifecycle.

Fibocom Unveils Intelligent Lawn Mower Robotic Solution with Linux-based Smart Module SC171 Integrated

The lawn mower robotic solution is a highly integrated solution equipped with Fibocom smart module SC171 and edge AI algorithm, empowering lawn mowers with unparalleled capabilities in environmental perception, precise positioning, map construction, path planning, autonomous obstacle avoidance, and seamless wireless connectivity. The revolutionary lawn mower robotic solution enables the autonomous navigation of the lawn mower without using boundary cables, significantly transforming the lawn mower industry. The core of this robotic solution lies in the AI-based lawn recognition algorithm, equipped with outdoor cameras, to achieve accurate detection and efficient planning of the lawn boundaries. It is worth mentioning that the solution also includes responsive obstacle avoidance and automatic recharging, in addition to the mapping function and an “edge-cutting mode” to facilitate precise mowing even at the sidelines. By deploying the solution, it will enhance the operational efficiency of the lawnmowers and reduce the time-to-market of lawn mower’s massive deployment in the global marketplace.

“We are proud to collaborate with Fibocom to help them develop these Linux-based edge AI solutions for industrial applications,” said Dev Singh, Vice President of Business Development and Head of building, enterprise & industrial automation at Qualcomm Technologies, Inc. “By utilizing Fibocom’s smart modules and Qualcomm Technologies’ powerful processors, Fibocom are enabling the integration of advanced edge AI technologies, empowering industries with responsive decision-making and real-time communication capabilities.”

“We have a clear vision for the edge AI-enabled future, and with the collaboration with Qualcomm Technologies, we will continue building the Linux-based edge AI-driven core solution for industrial-focused markets,” said Ralph Zhao, VP of MC BU at Fibocom.

“The first landed implementation in the robotic industry has infused confidence into the utilization of both Fibocom and Qualcomm Technologies’ strength to empower an intelligent, future-promising digitalized world.­­”

The post Fibocom to Unveil a Series of Linux-based Edge AI Solutions Mastering the Peak Performance for Industrial Applications Powered by Qualcomm Technologies 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.

Edge computing has emerged as a transformative technology for the Internet of Things (IoT), fundamentally altering how data is processed and managed within IoT ecosystems. By enabling data processing closer to the source, edge computing significantly enhances IoT infrastructure, leading to improved efficiency, reduced latency, and enhanced security. This article delves into the intricacies of edge computing in the IoT domain, exploring its impact and the potential it holds for the future of IoT.

Introduction to Edge Computing in IoT

The Internet of Things, a network of interconnected devices capable of collecting and exchanging data, has seen exponential growth in recent years. IoT devices range from simple sensors to complex industrial machines. Traditionally, IoT devices would send all collected data to centralized cloud-based services for processing and analysis. However, this approach often leads to high latency and increased bandwidth usage, which can be detrimental in scenarios requiring real-time data processing. This is where edge computing comes into play.

Edge computing refers to data processing at or near the source of data generation, rather than relying solely on a central data-processing warehouse. This means that data can be processed by the device itself or by a local computer or server, which is located close to the IoT device.

Enhanced Efficiency and Reduced Latency

One of the primary advantages of edge computing in IoT is the significant reduction in latency. By processing data locally, the need to send all data to a central cloud for processing is eliminated, thereby reducing the time it takes for the data to be processed and the response to be sent back. This is particularly crucial in applications where real-time processing is essential, such as autonomous vehicles, industrial automation, and smart grids.

Moreover, edge computing reduces the bandwidth required for data transmission, which is particularly important given the growing number of IoT devices and the massive volume of data they generate. By processing data locally and only sending relevant or processed data to the cloud, edge computing alleviates the strain on network bandwidth.

Improved Security and Privacy

Another critical aspect of edge computing in IoT is the enhancement of security and privacy. By processing data locally, sensitive information does not have to travel over the network to a centralized cloud, reducing the exposure to potential security breaches during transmission. Local data processing also means that in the event of a network breach, not all data is compromised, as some of it remains on the local device or edge server.

Furthermore, edge computing allows for better compliance with data privacy regulations, as data can be processed and stored locally, adhering to the legal requirements of the region in which the IoT device is located.

Enabling Advanced IoT Applications

Edge computing unlocks the potential for more advanced IoT applications. For instance, in the field of healthcare, wearable devices can monitor patient health data in real-time, processing and analyzing data on the spot to provide immediate feedback or alert healthcare providers in case of an emergency. In industrial settings, edge computing allows for predictive maintenance of machinery, where sensors can process data on the machine’s performance and predict failures before they occur.

Challenges and Considerations

Despite its advantages, implementing edge computing in IoT comes with its own set of challenges. One of the primary concerns is the management and maintenance of edge computing nodes. Unlike centralized cloud servers, edge devices are distributed and may be located in remote or hard-to-reach areas, making management and maintenance more challenging.

Additionally, ensuring the security of edge computing devices is crucial, as these devices could become targets for cyber-attacks. Unlike centralized data centers, which typically have robust security measures in place, edge devices may not have the same level of security, making them vulnerable.

The Future of Edge Computing in IoT

Looking ahead, the future of edge computing in IoT appears promising. With advancements in technology, edge devices are becoming more powerful, capable of handling more complex data processing tasks. This evolution is expected to drive further adoption of edge computing in various sectors.

In conclusion, edge computing represents a paradigm shift in how data is processed within IoT infrastructures. By enabling data processing closer to the source, it addresses the challenges of latency, bandwidth usage, and security. Although there are challenges in implementing edge computing, its benefits are significant, paving the way for more efficient, secure, and advanced IoT applications. As technology continues to evolve, edge computing is set to play an increasingly pivotal role in the IoT landscape, driving innovation and enabling new possibilities.

The post The Impact of Edge Computing on Data Processing and IoT Infrastructures appeared first on IoT Business News.

Splunk Edge Hub combats data deluge by bridging the data collection gap of physical and edge environments.

Splunk Inc., the cybersecurity and observability leader, today announced Splunk Edge Hub, a new solution that simplifies the ingestion and analysis of data generated by sensors, IoT devices and industrial equipment.

Unveiled at .conf23, Splunk Edge Hub provides more complete visibility across IT and OT environments by streaming previously hard to access data directly into the Splunk platform. Supported by Splunk partner solutions and optimized to work with the Splunk platform’s predictive analytics, Splunk Edge Hub enables advanced monitoring, investigation and response to help organizations drive digital resilience across their systems.

Data Deluge at the Edge

More organizations are realizing the significant benefits of edge computing. This distributed computing framework brings data transfer and storage closer to the data sources themselves to improve response times and save bandwidth. While edge computing is emerging as a driver of innovation, the process of identifying and gathering data in large quantities across multiple physical and virtual sources can be incredibly complex, tedious and costly.

Extends Splunk’s disruptive technology to highly fragmented environments

Splunk Edge Hub streamlines edge data collection and investigation by breaking down the barriers and silos of data access across physical and virtual environments and acting as a data aggregator from other vendors’ platforms. Working right out of the box, the device can be placed in a physical environment or on top of a customer’s existing OT hardware and easily configured to immediately collect, collate and stream data to the Splunk platform.

When combined with the Splunk platform, Splunk Edge Hub enables customers to:

• Monitor environmental conditions, including water, temperature, humidity and gasses to quickly and efficiently identify and remediate problematic conditions.
• Perform predictive analytics to identify anomalies in manufacturing processes and surface early indications of equipment maintenance needs or outages, to minimize operational downtime.
• Achieve more comprehensive visibility across IT and OT environments to better detect, investigate and remediate threats and IT stressors from a single platform.
• Build custom solutions through industry experts across environments that are historically difficult to extract data from, including transportation, oil and gas and supply chain, among others.

Supporting Quotes:

“At GrayMatter, we know getting business insight from your data is a challenge,” said Kemell Kassim, GrayMatter VP Cyber. “Partnering with Splunk allows us to facilitate data collection for customers and integrate in an easily consumable way.”

“Strategic Maintenance Solutions is thrilled to announce our partnership with Splunk to deliver the all-new Edge Hub,” said Jason Oney, President of Strategic Maintenance Solutions. “The Edge Hub enables us to provide our customers with an end-to-end solution for accessing industrial sensor, maintenance, and operations data at scale. With minimal configuration needed, data can now be seamlessly streamed into the Splunk Platform, allowing our customers to quickly start down the Industrial Transformation journey.”

“The University of Illinois Urbana-Champaign has been using Splunk for more than a decade as a part of our mission to serve our students, faculty, and researchers, so we were very interested in testing its latest offering, Splunk Edge Hub, to monitor our data center spaces,” said Nick Vance, Assistant Director, Data Innovation – Technology Services, University of Illinois Urbana-Champaign. “Spanning across 19,000 square feet, we are testing integrating sensors for room and rack temperatures and leak detection, ensuring we monitor temperature changes and flow as they fluctuate with infrastructure load. By sending data from these sensors into Splunk, we can more easily view it, expedite alerts and respond to issues before they become severe. The ability to stay ahead and respond quickly to any problems helps us protect tens of millions of dollars in equipment, so continually improving our monitoring technology is highly valuable for the University.”

“In today’s fast-paced business landscape, innovation is key to staying ahead of the competition. LG Electronics is leveraging Splunk Edge Hub to disrupt traditional industry models and drive innovation with edge computing and AI,” said Bongsu Cho, Vice President, AI & Big Data Division at LGE. “Splunk Edge Hub is enabling us to go beyond data and into automating our physical operations.”

“The only way to truly improve resilience is to be able to see everything going on within your organization,” said Tom Casey, SVP & GM of Products and Technology at Splunk. “Edge Hub is breaking down barriers and providing access to data that has historically been difficult to extract and integrate, to empower our customers with a level of visibility they have never had before. Our partners can use Splunk Edge Hub to build even more solutions across a multitude of industries that are tailor-made to their needs.”

Availability:

Splunk Edge Hub will be exclusively distributed through authorized domain expert partners who can tailor the solution to solve critical business and operational challenges within their industries. Splunk Edge Hub is currently available on a Limited Availability Release in the United States, with plans to expand to EMEA and APAC.

The post Splunk Introduces New OT Offering to Enable Visibility Across Physical and Industrial Environments appeared first on IoT Business News.

The 4th annual survey reveals an increased enterprise reliance on open source technologies to accelerate the adoption of IoT and Edge Computing solutions.

The Eclipse Foundation, one of the world’s largest open source foundations, today announced the availability of its 2022 IoT and Edge Commercial Adoption Survey report, based on an online survey of more than 260 IoT and edge professionals conducted from April 1st to June 15th, 2022.

The survey’s objective is to gain a better understanding of the IoT and edge computing ecosystems by identifying the requirements, priorities, and challenges faced by organizations that deploy and use commercial solutions, including those based on open source technologies.

Mike Milinkovich, executive director of the Eclipse Foundation, said:

“IoT and edge computing continued to accelerate in 2022 and into 2023 with no signs of slowing down, despite the current macroeconomic climate. These trends suggest that IoT and Edge are thought to be strategic investments that deliver true ROI. The open source model will only augment these benefits.”

Survey participants represent a broad set of industries, organizations, and job functions. Nine of the top conclusions drawn from the survey data include:

• IoT technologies are being adopted at an accelerated rate. 53% of respondents currently deploy IoT solutions and an additional 24% plan to deploy within the next 12 to 24 months, while 18% are currently evaluating deployments.

• Edge computing adoption is also on the rise. 53% of organizations are either utilizing or planning to utilize edge computing technologies within 12 months. Another 20% are currently evaluating the use of edge deployments.

• There is a shift towards higher investments into IoT & Edge. 23% of respondents project spending between $100K – $1M in 2022, growing to 33% in 2023. 10% anticipate spending over $10M and growing to 12% in 2023.

• There is a trend towards a larger number of IoT & Edge assets managed per deployment. Deployments of fewer than 1K managed assets will remain steady or decline, while larger deployments are on the rise. In terms of asset implementation, 52% are a mix of both greenfield and brownfield.

• More organizations now see IoT and edge as strategic, with spending decisions being driven at the executive level 38% of the time. This increased by 3% compared to the year 2021.

• 73% of organizations factor open source into their deployment plans. This clearly demonstrates that the dominant IoT & edge platforms will either be open source or based on open source. Only 27% of organizations using IoT and Edge technologies state they do not use open source technologies.

• The primary benefits of using open source according to respondents include: the ability to customize or influence code in projects (30%); flexibility (22%); as well as cost advantages (16%).

• The top 3 IoT and edge operational challenges are: 1) connectivity; 2) security; and 3) data collection & analytics.

• There is a trend towards a hybrid cloud strategy. 42% of respondents suggest that IoT deployments are using, or will use a hybrid cloud (i.e. composed of two or more distinct cloud infrastructures such as private and public).

The report also includes details on IoT and edge adoption by industry and the top concerns of commercial adopters.

The post The Eclipse Foundation Releases 2022 IoT and Edge Commercial Adoption Survey Results appeared first on IoT Business News.

Cypress Microcontrollers and Connectivity Devices, Software, Tools, and Ecosystem Partners Make IoT Product Development Faster, Lower Cost, and Lower Risk.

Cypress Semiconductor Corp. today unveiled solutions that give IoT product developers a simplified path to build high-quality, secure, and reliable IoT products.

The solutions, branded IoT-AdvantEdge, include connectivity devices and microcontrollers, software, tools and support, and capabilities from ecosystem partners to slash development complexity by solving critical IoT product design problems. With IoT-AdvantEdge, companies can overcome the challenges of wireless connectivity, device and cloud security, power consumption, device management and maintenance, component integration, consumer ease-of-use, human-machine interfaces, and platform monetization, to quickly bring reliable, secure, high-quality products to market.

“Building intelligent, connected products for the IoT edge is challenging. Getting wireless and embedded systems to work together and dealing with issues like security, cloud integration, and power management can be time-consuming and expensive,” said Hassane El-Khoury, president and CEO of Cypress.

“The problem solvers at Cypress have taken on this challenge and expanded our solutions to help our customers bring high-quality, secure, and reliable products to market faster. IoT-AdvantEdge simplifies the development process, bringing together the essential building blocks of the IoT through powerful software and hardware combinations.”

Cypress’ IoT-AdvantEdge solutions include:

• Devices: Cypress has a unique portfolio of microcontrollers, Wi-Fi, and Bluetooth/BLE devices that work in concert to support a broad range of IoT product requirements from battery-operated cameras to healthcare products. It integrates security and robust communication technologies and is used in many of the world’s most-sophisticated IoT products.
• Software: Robust software is fundamental to building high-quality, secure, and reliable products, and Cypress’ software is built for the IoT. Cypress’ ModusToolbox® development toolchain dramatically simplifies the development of Wi-Fi and Bluetooth/BLE IoT products with RTOS system MCUs like Cypress’ PSoC® family. ModusToolbox includes middleware empowering companies to connect their products to leading cloud-software platforms or to proprietary cloud services on public or private cloud infrastructure. In addition, Cypress’ open-source contributions into the Linux kernel are one of the reasons its Wi-Fi and Bluetooth products are so broadly used by IoT developers.
• Tools & Support: A comprehensive set of tools and support are critical to effectively address the unique challenges of IoT product design. Cypress’ tools – including low-power assistant, multi-radio smart coexistence, secure authentication, and over-the-air updates – substantially reduce the time and cost required to bring high-quality products to market. Cirrent (a Cypress subsidiary) provides IoT Network Intelligence (INI), a cloud-based analytics platform that delivers unprecedented insights into connectivity, networking, and other product-performance parameters for fleets of products in the field. Cypress engineers passionately support the IoT community and customers in tackling the challenges of building IoT products.
• Ecosystem Partner Capabilities: Building IoT products often requires capabilities from a broad ecosystem of suppliers. Cypress pre-integrates capabilities from a broad range of partners – including cloud service providers, application-specific semiconductor products, and applications developers – to help companies bring their IoT products to market faster.

As part of the launch of IoT-AdvantEdge, Cypress announced several new IoT products and resources:

• New Microcontrollers: Cypress expanded the PSoC 6 MCU family with two new memory configurations for the PSoC 62 and PSoC 64 Secure MCU lines: a high-performance, dual-core M4F/M0+ platform with 2MB flash and 1MB SRAM, and a cost-effective MCU with 512kB Flash and 256kB SRAM. These two new configurations give product companies more flexibility to choose the right configuration for their application.
• New Development Kits: Cypress’ new kits include low-power solutions that help designers tackle the challenges of complex connectivity, secure provisioning, secure firmware management, and cloud security. These integrated solutions are powered with ModusToolbox software and validated for AWS IoT Core and Pelion cloud applications, easing development of home automation and portable consumer devices.
  • PSoC 62 2M with 43012 Pioneer Kit: ultra-low power M4/M0+ MCU with dual-band 802.11n Wi-Fi and Bluetooth® 5.0
  • PSoC 62 512K with 4343W Prototyping Kit: cost-effective HMI MCU with 802.11n Wi-Fi and Bluetooth 5.0
  • PSoC 64 2M Secure Boot with 4343W Pioneer Kit: enables secure cloud connectivity and provisioning, and secure firmware management
  • Cypress-Azurewave Module Pioneer Kit: pre-certified module delivers a turnkey connected MCU platform that is easily integrated into any product
• New Software: ModusToolbox combines Cypress’ secure compute and connectivity expertise in a common software tool, making it easier for IoT developers to bring successful products to market and to support them through the lifecycle. Cypress released ModusToolbox version 2.1, which expands its cloud support beyond AWS IoT Core and Pelion services to include specific support for customers that have built their own cloud backends. In addition, ModusToolbox now supports five popular IDEs so developers can use the environment of their choice.
• New Web Resources: Cypress launched IoT-AdvantEdge on Cypress.com and a new IoT Developer Zone for its online developer community. The Cypress Community offers robust discussion forums, technical blogs and a resource library for visitors around the globe. The Cypress Community also helps customers, developers, and partners connect with their peers, access a vast depth of resources, and ultimately bring high-quality, secure, and reliable products to market faster.

“We are driven to make IoT technology ubiquitous, with Cypress solutions in every IoT device,” El-Khoury said. “IoT-AdvantEdge solutions reflect our commitment toward achieving that goal.”

The post Cypress Unveils IoT-AdvantEdge™ Solutions Providing Developers a Trusted Design Path to IoT Edge Products appeared first on IoT Business News.

• First platform of the LoRa Edge portfolio is a geolocation solution revolutionizing IoT devices for asset management
• Platform integrates an ultra-low power LoRa® transceiver, GNSS and Wi-Fi scanning technologies
• Combining LoRa Edge solutions and Cloud-based geolocation services creates a unique system architecture that offers the best balance between location accuracy and low power consumption

Semtech Corporation has today announced LoRa Edge, a new highly versatile and low power software defined LoRa®-based platform that will enable a wide portfolio of applications for indoor and outdoor asset management, targeting industrial, building, home, agriculture, transportation, and logistics markets.

The first product from this portfolio is a geolocation solution that revolutionizes the development of Internet of Things (IoT) devices for asset management applications, featuring low power Wi-Fi and GNSS sniffing capabilities combined with simple to use and cost effective LoRa Cloud geolocation and device management services to significantly reduce the cost and complexity of locating and monitoring IoT assets.

“Semtech continually delivers Internet of Things (IoT) solutions that simplify and accelerate the development of LPWAN applications,” said Pedro Pachuca, Director of IoT Wireless in Semtech’s Wireless and Sensing Products Group.

“LoRa Edge and LoRa Cloud geolocation services enable customers to develop ultra-low power applications for a variety of industries and will expand the mass adoption of LoRa in the IoT ecosystem.”

Over the next decade, 500 billion devices are expected to connect to the internet (Cisco), as organizations continue to shift towards a more IoT-focused business strategy, and the majority of those IoT devices require some form of localization capability either at point of install or through the asset’s life.

The LoRa Edge geolocation platform will enable solution providers to leverage the unique localization capabilities of LoRa as well as GNSS and Wi-Fi scanning capabilities from a single chip solution, allowing customers to choose the best localization tool for the application task they are addressing.

By removing the need for incremental GNSS and Wi-Fi components, LoRa Edge reduces the bill of material (BOM) costs of devices and significantly reduces design and procurement complexity. With the addition of LoRa Cloud geolocation services, providing easy-to-use and cost effective TDOA, GNSS and Wi-Fi-based location calculation in the Cloud to dramatically reduce device power requirements and improve asset management efficiency, LoRa Edge enables customers to further manage total cost of ownership (TCO), paying only when they need an asset to be located. The best-in-class key provisioning at point of manufacture and a secure join process further simplifies the development of IoT solutions, which adhere to customers exacting expectations of security.

The first LoRa Edge chipset targeted with geolocation (LR1110) is available today and more products from this portfolio will be released in the first half of 2020.

Key Product Features:

Multi-Purpose Radio Front-End

• 150 – 2700 MHz continuous frequency synthesizer range
• GPS/BeiDou scanning
• Wi-Fi passive scanning

Low-Power LoRa/(G)FSK RF Transceiver

• Worldwide frequency bands support in the range 150 – 960 MHz
• High power PA path +22 dBm
• High efficiency PA path +15 dBm
• Fully compatible with the LoRaWAN® standard

Cryptographic Engine

• Hardware support for AES-128 encryption/decryption based algorithms
• Handling device parameters such as DevEUI and JoinEUI
• Protects confidential information such as encryption keys
• Stores NwkKey, AppKey, as defined in the LoRaWAN standard

The post Semtech Releases a New Portfolio of Solutions, LoRa Edge™, to Simplify and Accelerate IoT Applications appeared first on IoT Business News.

Internet Of Things, commonly called IoT, refers to the connection of daily devices like cars, home appliances, and industrial devices to the internet. As Gartner predicts IoT will reach 26 billion connected devices by 2020. One of the challenges in IoT is to capture, analyze and gain insights from data from this massive volume of devices effectively and efficiently. Most Internet Of Things Devices equipped with sensors have two parts- firstly, a front-end application or a device like Coffee Maker; Door lock (generally closer to the consumer) and secondly, a cloud where the data from the device goes and is then processed to build context (generally remote to the consumer). The first one is referred to as “IoT Edge” and the second one is called the “IoT Cloud.” The activities happening on the IoT Edge part is known as “Edge Computing.”

Microsoft, CISCO, IBM, Dell, and many startups are championing Edge computing; now that represents a shift in IoT implementation architecture. In Edge computing, data intelligence happens on the Edge instead of the Cloud which resulted in localizing certain kinds of data analysis and decision-making. Edge computing enables quicker response times, resolves network latency and reduced data traffic by sending only selective data to the cloud. Edge computing helps to achieve better efficiencies.

Two major components in Edge computing are Edge computing hardware and Edge computing software. The processing power of the Edge hardware plays a crucial role in determining the Edge software capability. Usually, a high computing device like a desktop or server or custom Edge hardware is a good choice. Edge software typically comprises of components that are required to establish the connection with the device sensors like Wifi, Bluetooth, ZigBee, Z-Wave and components needed to extract and store data from the device sensors like database.

The third components are necessary to perform data analysis like data analytics and machine learning. Edge Analytics and Edge Machine learning have been around for a few years now. There are many open source and proprietary software in the market. However, they had limited use for the last few years due to the complexity of including them in low compute power legacy IoT network solutions. AWS Edge and Azure Edge are a couple of prominent Edge software from Amazon and Microsoft. Apache Kafka and Scikit are open source implementations of Edge Analytics and Machine learning respectively.  

Of late, the Edge software package that has been making waves is Edge AI. Edge AI is Artificial Intelligence capability on the IoT Edge and is also a prime place for more innovation, as  adding these advanced software capabilities into a limited computing power resource is considered to be technically challenging. The goal of Edge AI is to understand, learn and act on the data from the IoT devices without any human intervention.

Edge Software is taking center stage in IoT applications due to its ability to offer lower network latency, faster reaction times on the IoT data and lower cost of operating on the data without much support of cloud computing. Because of this reason, we will start seeing them more in legacy and new IoT applications. We will see all IoT platforms, both of established players and startups embracing Edge software into their platform offerings. The hyper-scale cloud players like AWS, Microsoft and Google have been slow to enter the Edge software space; solely because Edge can have negative effect on their IoT cloud revenues. However, the technology and business case for the Edge software, as discussed above, has become prominent recently, that nobody can afford to ignore the role of Edge. Hence, this is forcing the IoT cloud players to explore newer revenue models that includes both IoT Cloud and IoT Edge Date intelligence revenue streams rather than riding only on the IoT cloud revenue stream.  

Edge Data Intelligence has become a key part of Enterprise IoT strategy already and there is lot of technology advancements happening. This will continue to have a positive impact on the IoT applications as the industry gets into a mass adoption phase.

The post A Business Case for IoT Edge Data Intelligence appeared first on IoT Business News.