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.

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.

Gartner expects that by 2022 more than 50 per cent of enterprise-generated data will be created and processed outside the traditional data center or cloud. With the explosion of the Internet of Things (IoT), the amount of data devices collect is becoming so vast that it cannot all be stored and monitored in the cloud. This is why industry needs to invest in edge solutions.

Martin Thunman, CEO and co-founder of the leading low-code platform for streaming analytics, automation and integration for industrial IoT, Crosser, explains more.

As industry migrates its data to the cloud at an ever-quickening pace, sending every fragment of raw data, and from every single machine, will not deliver value. IoT technologies can benefit every industry — whether that’s connected cars, smart factories, or even retail environments. To get this data faster and in a more secure and cost-effective way, companies need to turn to edge computing.

On the edge

Low code and edge computing are two of the biggest current technology trends. At its basic level, edge computing brings data storage closer to the device where it’s being gathered. An edge gateway, for example, can process data from an edge device, then send only the relevant data back through the cloud to reduce bandwidth needs.

Alternatively, it can send data back to the edge device to meet real-time application needs. Low code platforms increase productivity and bridge different departments and skill sets within an IoT project, enabling innovation for both developers and non-developers in the same environment.

Founded in 2016, Crosser describes its own platform as an interface that simplifies development, innovation and collaboration in its users’ internal teams. Its low code-based software handles analysis, automation and integration so that users do not have to send more data than necessary to the cloud.

Specifically, Crosser aims to provide edge solutions to the manufacturing industry. This is because, generally speaking, operations in manufacturing are complex and plant managers have access to a lot of data. But often they are unsure how to manage it.

Untapped potential

Currently, only 32 per cent of data available to enterprises is put to work. The remaining 68 per cent goes unleveraged, according to research by Seagate Technologies. With so many connected assets, manufacturers see the value in collecting operational data. However, the raw data that’s collected is not providing the solution it was intended to offer — that of a decision-making engine. Organizing this data, and taking what’s useful, will help plug the gap that’s causing manufacturing to fall behind in its digital development.

Crosser was founded to help businesses handle their data at the edge of their networks. As a relatively new company that’s growing in an area of innovation, Crosser adapts a laser focus to stay ahead of competition. We must assume that the technology we’re developing today will be in the hands of competitors in a few years’ time — and we need to continue to adapt to keep ahead.

Industry is recognizing that not all data needs to head up to the cloud and, with continued investment and innovation, Crosser is ready to support manufacturers as they start to do more with their data.

The post Uncovering the untapped potential of data appeared first on IoT Business News.

A survey by Yokogawa found that more than half of decision makers from global process industries are increasing their investments in industrial autonomy. Following recent analysis of how its customers are using its visual drag and drop Flow Studio, Goran Appelquist, CTO at edge analytics software company Crosser, provides insight into the five most popular analytics modules being used to create data flows in its platform.

One of Crosser’s aims is to fight complexity with simplicity, and this is evident in the Flow Studio¹. Usually, monitoring an advanced data flow requires various skill sets to manage each input, but the Flow Studio minimizes the need for additional software developers and data science teams. Pre-built modules don’t require written coding and the stream of the flow can be viewed together and can be managed by one individual. Low code solutions also ensure higher code quality because of the extensive testing that the combined user group is performing.

Let’s examine the five most popular analytics modules.

Property Mapper

Described as the ‘Swiss army knife’ of data transformation, Property Mapper is the most commonly used module in the Flow Studio.

It’s unlikely to receive usable data straight away, so this module restructures data into a required format, introduces structure if data is presented unstructured, aligns naming conventions and adds metadata. It only operates on the structures, without altering the values.

Property Mapper simplifies the processing of data by harmonizing it from multiple sources and treating designated data as one stream. It also supports the scenario of numerous outputs of data that are being sent to multiple destinations, which all require data in different formats, by restructuring data on the way out of the flow.

Python Bridge

All programmers are familiar with Python, the high-level programming language that optimizes code readability. However, this module enables Python code to run as part of a flow and install any third-party libraries. It also makes it easier to write Python in comparison to searching for an ideal pre-built module to execute what’s required. Most Python modules can be used alongside transformations that the Property Mapper supports.

Python Bridge is perfect for running machine learning (ML) models because most are built and trained using one of the Python ML frameworks — to execute those models you need to replicate the same ML framework at the edge as a resource. This is an important part of the Edge MLOps strategy.

Text Template

There’s no point in creating a data flow if you can’t communicate the actions associated with that information. Text Template creates dynamic text messages to combine static text with data from your messages. It has two key functions within a flow.

The first is that it creates human readable notifications for monitoring a condition. When that condition triggers, the notification informs a member of the team, who can act on that trigger.

It’s second function is to combine multiple stream values into a single value. For instance, communication over APIs requires specific values based on multiple input values and data, including numerical values, must be sent as a string.

TimeStamp

When data is received, there is no record to confirm the time it was captured. In a data flow, it’s important to monitor the waiting time during code execution, as well as measure the efficiency of your code. This is where the TimeStamp module is used to stamp data with the time of capture.

On occasion, time stamps are reported, but not in the appropriate format. TimeStamp can also convert incoming timestamps to the format required by the destination.

Array Split and Join

Array Split is the fifth most used analytics module in the Crosser Flow Studio, although Array Join is used in unison with Split. Arrays are the common format used when data is retrieved from multiple sensors of a PLC or API. Working with arrays is a very common operation within Industrial IoT.

This multisensory data is presented in one large message where sensor values are presented as an array. The Array Split module breaks up the array into individual messages and applies some processing, while Join does the opposite of recombining a stream of messages into an array.

Analyzing how customers use the Flow Studio gives great insight into the functions that matter most to them. IoT operations are complex, but with the perfect recipe of analytics modules, our Flow Studio removes the common complexity and allows a comprehensive understanding of how to create an advanced flow — without the need for surplus staff.

The post Exploring the most used Low Code modules for Edge Analytics appeared first on IoT Business News.

Rather than blockchain usurping the world, it seems that the IoT is going to give blockchain its consolation prize. Along with distributed ledger technology in play at various levels and in various applications, the unfolding IoT has other regular friends and emerging stars. Edge computing and 5G tech are two important development areas right now – significant components of the IoT that they are – but a host of realities beyond them are also going to find development and prominence as the IoT continues apace.

It’s perhaps fitting that the IoT is primarily manifesting in the world of commerce and industry. Business is always hungry for efficiency, productivity, and a reduced wage bill. Those with the capital to outlay are automating as far as possible and employing Business Intelligence or IoT-As-A-Service (IAAS) providers to manage the ensemble. A surprising number of smaller manufacturing concerns are also implementing IoT solutions, initial outlay notwithstanding. If ever there was a time to have a trusted IT company on board, this is it. Hardware is stealing software’s thunder, and the cloud sees all.

Business will streamline the IoT into efficient and practical functionality before it’s passed onto consumers to change their homes – and worldview – in short order. Already we’re on the right rung of the ladder. Wearable devices and 5G alone have already shifted the consciousness of older consumers. For the younger ones, it’s just the way things are, the world they’re growing up in. Data latency is disappearing as edge computing and 5G particularly enable a different, more accurate experience of data as a whole. The promises of the Noughties around data lake supremacy were empty, but they’re going to come home with the IoT.

For decades, business has been collecting data and, although it was voluminous, it was also – in many instances – too much for anyone to mine effectively. AI is changing that, although AI is largely dependent on the data we’ve assembled to instruct it – and the majority of that data is dark. The IoT is introducing billions of qualification points to that reality, and so the quality of data will become far better and – in a feedback loop – so will AI. Rather than big corporates sitting on top of a mountain of data, now millions of organizations can have a synchronized hardware fraternity – the IoT’s devices – collating data in a far more accurate and qualified manner. Gone are the old lakes of potential – enter the IoT – and a new, incredibly detailed yet thoroughly sorted data collection is being made possible.

Big data redefined

The International Data Corporation has postulated that there will be over 40 billion IoT devices by 2025, and they’ll generate just under 80 zettabytes of data. Now that is big data. The IoT doesn’t come without challenges, however. A great many commercial entities are jumping onto the IoT, but lack a clear strategy for deployment, or even a ‘why.’ Like many other technologies, the IoT holds massive potential for efficiency and streamlined operations, not to mention even bigger piles of data, now stacked and ordered as never before. That’s still no imperative for every business to assume it can glean those efficiencies without an idea of how it plans to do so, and how much value it anticipates from implementation.

Nonetheless, in a modest replay of the kind of must-have insistence of the dotcom era, the majority of businesses (including SMEs) today acknowledge that it’s no longer ‘if’ they’ll assimilate the IoT, but ‘when.’ Sadly, almost a third of current IoT business projects fail, because the organization hasn’t formulated an unambiguous business case for IoT. Further, those that succeed are seeing an ROI of no more than 15 percent on average. Clearly, the IoT is still young, and as implementation quickens and competing organizations put some thought into it, ROI ought to escalate substantially. Big data and AI are clear front runners in the current IoT. Massive data volume and an artificial intelligence to sort it are tools business has never employed together until now. Big Data techniques combined with AI allow for data to be accessed in real time. Commerce is stumbling through the IoT at times, but slowly moving past the mistakes to find value, value that will inform consumer applications over the next two years.

Notwithstanding the dominance and security of SaaS as the default option for modern business, security issues will rise with the IoT. Blockchain technology has already found firm footing in addressing the security challenges of the IoT. Thanks to its completely secure smart contract component, its application for the IoT is generous. From multiple devices – protected by blockchain protocols – data can be presorted and collated on the edge of the network – edge computing. Thus suitably sorted and made sensible, part of that data will go to the cloud via 5G. At every step, however, security will have to be extremely tight for the IoT not to falter in front of the public. The cryptocurrency arena was awash with heists post 2010. If the IoT isn’t to suffer similar bad press sufficient to slow adoption, developers will have formulated smart protocols for users.

Blockchain technology has already been successfully applied in innumerable cases, some of them large and ambitious. While it suffered a setback from organizations that didn’t find any value in the tech, nonetheless fintech, governing regimes and many other big hitters have fostered the tech extensively. The IoT’s blockchain technology trend looks set to grow.

And what will the IoT be to Mrs. Jones?

Smart home appliances are set to soar in prevalence and popularity. Even for those who abhor the notion of a push-button paradise, the IoT’s charm often wins them over. At some point – around mid-2021 – the focus of many providers (and the press) will shift to the consumer marketplace, as the IoT dazzles homeowners across the globe.

We can expect a sharp upswing thereafter in the capabilities and comforts of domestic IoT devices. Fairly rapidly, thanks to refined applications in commerce and industry and concerted competition between retail providers, the innovation of home-based IoT devices will rise, along with their possibilities for consumers. Security protocols – likely mostly blockchain, but quite possibly with important components of other tech – will have been sorted in the business arena, and will enhance both the safety and security of retail consumers.

IoT security will gain a boost in prominence on the back of consumer fears of lost privacy and corruption, as the cyber criminal fraternity will have evolved to take advantage of a few billion new doors to knock on. Rather than security on the IoT, an IoT security will emerge – a book of best practice which, much like software patches and antivirus apps, will define security for the users of the IoT.

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44% of companies currently using Edge Computing in IoT, expected to grow to 59% by 2025.

Edge Computing is on the rise in IoT deployments and is expected to show solid growth over the coming years, according to Strategy Analytics report: Edge Computing: Decentralizing for Performance in the IoT.

Strategy Analytics believes that data will be processed (in some form) by edge computing in 59% of IoT deployments by 2025. The driving forces in this assumption are the key benefits derived from edge computing, namely more efficient use of the network, security and response time.

Currently, Strategy Analytics’ End User research suggests that 44% of companies are currently using edge computing, in some form, in their deployments.

Andrew Brown, Executive Director Enterprise and IoT Research commented:

“While the cloud represents an increasingly common model for analyzing IoT data, enthusiasm is growing for edge computing, where data can be analyzed and filtered at the edge of the network. Doing so can lead to benefits in delivering a faster response from analyzed data.”

“Taking a more efficient and optimized approach in terms of what data is sent to the cloud, with reductions in traffic volumes, has positive net effects both on the security of the data being sent and the cost of sending data to the cloud.”

David Kerr, SVP Global Wireless Practice, added:
“While Edge benefits include faster response times, improved application performance, reduced network traffic, security and reduced demand on cloud infrastructure, there remain challenges that must be considered as well. These include immaturity of the current market and perceptions among customers that they have no need to change their current setup. Other issues include a lack of familiarity with edge computing for IoT environments and a lack of transparency over the additional costs that could be incurred.”

What do you consider the benefits of Edge Computing to be? – Base: Those who spent on IoT: Overall- 1430; US – 273; UK – 287; France – 269; Germany: 300; China – 301

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Hailo Delivers Unprecedented Performance Enabled by Innovative Processing Architecture Specifically Designed for Deep Learning Applications. The Paradigm-Changing Chip is Being Sampled with Select Customers.

AI chipmaker Hailo released today the Hailo-8, the world’s top performing deep learning processor.

Hailo is now sampling its breakthrough chip with select partners across multiple industries, with a focus on automotive. The chip is built with an innovative architecture that enables edge devices to run sophisticated deep learning applications that could previously run only on the cloud.

Key disadvantages exist in the current architecture of the embedded processing infrastructure, designed based on a 70-year-old underlying structure. Hailo addresses these issues with its holistic solution, which completely rethinks the existing pillars of computer architecture – memory, control, and compute – and incorporates a key, comprehensive Software Development Kit (SDK) co-developed with the hardware.

The Hailo-8 processor, which features up to 26 tera operations per second (TOPS), significantly outperforms all other edge processors with area and power efficiency far superior to other leading solutions by a considerable order of magnitude – all at a size smaller than a penny, including the required memory. By designing an architecture that relies on the core properties of neural networks, edge devices can now run deep learning applications at full scale more efficiently, effectively, and sustainably than traditional solutions, while significantly lowering costs.

Hailo is working with leading OEMs and tier-1 automotive companies in fields such as advanced driver-assistance systems (ADAS), as well as players in industries like smart cities and smart homes, to empower smarter edge and IoT devices. These industries often require the use of high-performance cameras to perform tasks such as semantic segmentation and object detection in real time – tasks which Hailo’s chip can perform at full resolution, while consuming only a few Watts. Hailo’s redesign eliminates untenable heat dissipation issues and removes the need for active cooling systems in the automotive industry. Its advanced structure translates to higher performance, lower power, and minimal latency, enabling more privacy and better reliability for smart devices operating at the edge.

According to preliminary results comparing Hailo-8 to Nvidia’s Xavier AGX, which runs NN benchmarks such as ResNet-50, Hailo-8 consumes almost 20 times less power while performing the same tasks.

Device	Resolution	FPS	Total Power [Watt]	Total Power Efficiency [TOPS/W]
Hailo-8	224×224	672	1.67	2.8
Nvidia Xavier AGX	224×224	656	32	0.14

Orr Danon, CEO of Hailo, said:

“In recent years, we’ve witnessed an ever-growing list of applications unlocked by deep learning, which were made possible thanks to server-class GPUs. However, as industries are increasingly powered and even upended by AI, there is a crucial need for an analogous architecture that replaces processors of the past, enabling deep learning to run devices at the edge.”

“Hailo’s chip was designed from the ground up to do just that. We are excited to help customers drive their intelligent devices to new limits. A new age of chips means a new age of technology.”

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Under the IIC umbrella, the combined membership will accelerate the adoption of the IIoT, fog and edge computing.

The Industrial Internet Consortium® (IIC) and the OpenFog Consortium® (OpenFog) today announced that they have agreed in principle to combine the two largest and most influential international consortia in Industrial IoT, fog and edge computing.

The move will bring OpenFog members into the IIC organization at a time when their complementary areas of technology are emerging in the mainstream.

The combined memberships will continue to drive the momentum of the Industrial Internet including the development and promotion of industry guidance and best practices for fog and edge computing. The organizations expect the details to be finalized in early 2019.

“This is great news for the industry. Both organizations have been advancing the IIoT, fog and edge computing, and their members represent the best and the brightest in their fields. It makes sense to merge their expertise and work streams to continue providing the IIoT, fog and edge guidance that the industry needs,” said Christian Renaud, Research Vice President, Internet of Things, 451 Research.

IIC President Bill Hoffman, said:

“The Industrial Internet Consortium, now incorporating OpenFog, will be the single largest organization focused on IIoT, AI, fog and edge computing in the world. Between both of our organizations we have a remarkable global presence with members in more than 30 countries.”

“This agreement will help accelerate the adoption of the IIoT, fog and edge computing.”

The Industrial Internet Consortium is the world’s leading membership program transforming business and society by accelerating the Industrial Internet of Things. The OpenFog Consortium was founded to advance fog computing and address bandwidth, latency and communications challenges associated with IoT, 5G and AI applications.

“We’re excited by the growth and advancement of fog technologies–from a technology, standards and general awareness standpoint—since our launch nearly three years ago,” said Matt Vasey, OpenFog chairman and president, and director, AI and IoT business development, Microsoft.

“During that time, it has increasingly become apparent that we share so much synergy with the efforts of the IIC that it just made sense to bring the two consortia together. The resulting combination of memberships, resources and shared knowledge will only further the growth of the technologies, including fog, that will support IIoT ecosystems.”

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New Service Enables Best Practice for Edge Computing: Localize Compute, Data and Applications for Real-Time Value.

Nutanix, Inc., a leader in enterprise cloud computing, today announced the general availability of Xi IoT, a new edge computing service offered as part of the company’s Xi Cloud Services.

Combining the simplified elegance of core Nutanix solutions with a streamlined approach, Xi IoT eliminates complexity, accelerates the speed of deployment and elevates developers to focus on the business logic powering IoT applications and services.

In 2017, 3 billion enterprise IoT edge devices generated up to 30 times more data (256 ZB) than the 30+ million nodes across public and private cloud data centers. But the current IoT model in which the massive amounts of data on edge devices is sent back to a centralized cloud for processing has severely limited the ability of customers to make real-time, actionable decisions from intelligence gained at the edge. For customers, deriving value from this massive amount of data is rife with latency issues, lack of scalability, lack of autonomy, and compliance and privacy issues.

Unlike traditional IoT models, the Xi IoT platform delivers local compute, machine inference, and data services to perform real-time processing at the edge. Xi IoT Data Pipelines can securely move intelligently analyzed data to a customer’s public (Azure, AWS or GCP) or private cloud platform of choice for long-term analysis. Edge and core cloud deployments all operate on the same data and management plane, so Xi IoT customers have seamless, simplified insight into their deployment.

Because Xi IoT provides zero-touch setup and management of edge devices, organizations can eliminate the risk of IoT security breaches due to human error, increase overall efficiency and reduce the cost of operating edge devices across the globe.

Through Xi IoT, customers can manage all their edge locations through a sophisticated infrastructure and application lifecycle management tool, regardless of platform. Developers can leverage a rich set of popular open APIs to deploy next-generation data science applications as containerized applications or as functions, which are small snippets of code. This can be integrated into existing CI/CD pipelines, allowing them to make changes quickly from a single location.

By leveraging this well-known framework, Xi IoT helps IT organizations reduce training, development and testing costs while eliminating the possibility an organization is locked in to one public cloud provider. And because data is processed in real-time at the edge, companies are no longer inhibited by the transmission of data back to a core datacenter for processing, so decisions can be made based on data autonomously and in real-time.

Ashish Nadkarni, Group Vice President, Infrastructure Systems, Platforms and Technologies, IDC, said:

“It is critical for enterprise organizations to have an edge strategy to unify, edge to cloud connectivity, to rapidly build IoT applications, and to provide real-time analytics closer to where the data is generated.”

“For companies looking for a competitive advantage, solutions like Nutanix Xi IoT can help them stay ahead of the market and identify new opportunities to grow their businesses.”

Nutanix Xi IoT will focus on the manufacturing, retail, oil and gas, healthcare, and smart cities markets at launch.

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