JV Utilises Deep Learning & Edge Computing To Make Factories Smart

Hitachi is aiming to utilize deep learning and edge computing technologies to make machines intelligent, in order to improve productivity. In pursuit of this, it has formed an automation joint venture with industrial robot and factory automation company Fanuc and AI-startup Preferred Networks.

Intelligent Edge System will utilize AI technologies in the social and industrial infrastructure field. It will develop fast, real-time control systems for network-connected industrial robots and machine tools. These control systems will leverage deep learning AI technology to become smarter over time as linked machines manufacture products.

Preferred Networks will use its deep learning AI technology to process information more efficiently and speed up data analysis. This is hoped to boost production line productivity and allow robots to recognize things and adjust their moves accordingly. Robots will also be able to automatically take on the task of an adjacent robot on the production line in case it breaks down.

Edge computing will help the initiative by handling the task at the edge of the network instead of centrally processing data. This will let machines on the production line process the massive amount of data, such as the movement of mechanical hands, on the spot.

Preferred Networks has already applied its AI expertise for Toyota Motor and Nippon Telegraph & Telephone. Toyota Motor invested in the startup for the development of autonomous vehicles that can learn various driving conditions by processing data by themselves rather than relying on cloud computing.

http://www.hitachi.com/New/cnews/month/2018/01/180131f.pdf

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IoT gateways – Drivers for fog computing

Connected devices are becoming essential components for enterprises as they can drive significant connectivity and integration between systems and data. The increasing number of devices getting connected to each other generates a huge amount of data.

However, when it comes to leveraging the full potential of these connected devices and data, it is necessary to have a scalable and robust environment which allows faster processing of data between systems, says Mohit Bhardwaj, Digital Marketing executive, eInfochips.

The fundamental concern is on how to efficiently manage this data, as any data loss or delay in processing of data from a connected ecosystem can cause critical damage to an enterprise’s workflow.

Role of IoT gateway edge analytics in data processing & management

IoT Gateway is the key to any IoT deployment. It is a bridge between IoT devices and cloud that enables remote control of the devices and machines. The increasing number of devices propels the requirement for IoT gateways to solve the data management issues with Edge Analytics.

Edge analytics with IoT Gateway allows data processing before it is transmitted to the cloud. The gateway collects all the data from the connected devices and executes necessary algorithms or rule engine on it and sends actionable commands to connected devices. The actions allow for response to be taken in real-time and also helps in self-healing mechanism during faults/errors.

In large enterprises, having multiple geographical spread, there are a huge number of connected devices and generated data. This heterogeneous data, distributed at different levels (Devices and machines ) have high latency in cloud transferring due to the uncontrolled data flow. Here, distributed edge analytics is the solution as it allows faster data transfer and processing, resulting in the reduction of latency.

AWS Greengrass is the best example for the edge analytics setup. It allows enterprises to run local compute, messaging, data caching, sync, and ML inference capabilities for connected devices in a secure way. Greengrass ensures quick response of IoT devices at the time of local events, that reduces the cost of transmitting IoT data to the cloud.

How distributed edge analytics works in larger geographical areas

Let’s take an example of smart grids to understand the concept in-detail.

Smart grids are the combinations of smart meters, smart appliances, renewable energy resources, energy efficient resources, and substations. In a particular city area, the number of smart meters is equivalent to the number of households in that area. These AMI (Advanced Metering Infrastructure) continuously collects the energy consumption data and route it to the IoT gateways. The gateway enables edge analytics and then the processed data is rerouted to the cloud by the gateway.

As the number of AMI is high in a particular area, the number of gateways will be proportionately higher.

Merits of distributed edge analytics:

Reduced data transfer latency
Fast access to the faulty areas
Quick functional recovery and self healing capabilities that brings resilience in the system

Distributed edge analytics also enables fast response to the cloud in case of faults and failures with Fog Computing so that the recovery time can be minimal. Let us understand how.

How fog […]

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Five key IIoT predictions for 2018: Collaboration, customer success, edge computing, and more

The global industrial IoT market is set to reach $ 933 billion by 2025, according to Grand View Research. Here, Sastry Malladi, CTO of FogHorn Systems, outlines what he think will happen in the space in 2018.

Momentum for edge analytics and edge intelligence in the IIoT will accelerate in 2018

Almost every notable hardware vendor has a ruggedized line of products promoting edge processing. This indicates that the market is prime for Industrial IoT (IIoT) adoption. With technology giants announcing software stacks for the edge, there is little doubt that this momentum will only accelerate during 2018. Furthermore, traditional industries, like manufacturing, that have been struggling to showcase differentiated products, will now embrace edge analytics to drive new revenue streams and/or significant yield improvements for their customers.

Additionally, any industry with assets being digitized and making the leap toward connecting or instrumenting brownfield environments is well positioned to leverage the value of edge intelligence. Usually, the goal of these initiatives is to have deep business impact. This can be delivered by tapping into previously unknown or unrealized efficiencies and optimizations. Often these surprising insights are uncovered only through analytics and machine learning. Industries with often limited access to bandwidth, such as oil and gas, mining, fleet and other verticals, truly benefit from edge intelligence. What’s more, those that apply edge intelligence are able to benefit from real-time decisions, as well as insights from voluminous streaming sensor data.

Due to the current pain points in the IIoT space and the edge technology availability to address them, we expect to see increased interest in edge analytics/ML from oil andgas, energy, utilities, transportation and other sectors interested in revamping their IIoT value.

Business cases and ROI are critical for IIoT pilots and adoption in 2018

The year 2017 was about exploring IIoT and led to the explosion of proof of concepts and pilot implementations. While this trend will continue into 2018, we expect increased awareness about the business value edge technologies bring to the table. Companies that have been burned by the “Big Data Hype” – where data was collected but little was leveraged – will assess IIoT engagements and deployments for definitive ROI. As edge technologies pick up speed in proving business value, the adoption rate will exponentially rise to meet the demands of ever-increasing IoT applications.

IIoT standards will be driven by customer successes and company partnerships

IIoT is just now getting attention from the major technology players. If anything, 2018 will see more new products coming to market, and there will be more to choose from in terms of standards. The next year or two will see stronger alliances, unlikely partnerships and increased merger and acquisition activity as the large technology companies seek innovation inside and outside their organizations. As for standards, they will be driven by success of customers and patterns of scalable IIoT solutions.

IT and OT teams will collaborate for successful IIoT deployments

IIoT deployments will start forcing closer engagement between IT and operations technology (OT) teams. Line of business leaders will get more serious around investing in digitization, and IT will become the cornerstone required for the success of these initiatives. What was considered a wide gap between the two sectors – IT and OT – will bridge thanks to the recognized collaboration needed to successfully deploy IIoT solutions and initiatives.

And will OT design affect IIoT apps? Yes, definitely. Recent research and field studies suggest that analytics tools are being made more accessible to end users, i.e. domain experts and plant operators. This means that advanced technology is now being made available to field workers, so operational decisions can be driven in real-time at the industrial location.

Edge computing will reduce security vulnerabilities for IIoT assets

While industries do recognize the impact of an IIoT security breach there is surprisingly little implementation of specific solutions. This stems from two emerging trends:

  • Traditional IT security vendors are still repositioning their existing products to address IIoT security concerns
  • A number of new entrants are developing targeted security solutions that are specific to a layer in the stack, or a particular vertical

This creates the expectation that, if and when an event occurs, these two classes of security solutions are sufficient enough. Often IoT deployments are considered greenfield and emerging, so these security breaches still seem very futuristic, even though they are happening now. Consequently, there is little acceleration to deploy security solutions, and most leaders seem to employ a wait-and-watch approach. The good news is major security threats, like WannaCry, Petya/Goldeneye and BadRabbit, do resurface IIoT security concerns during the regular news cycle. However, until security solutions are more targeted, and evoke trust, they may not help move the needle.

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NXP and Alibaba Cloud Announce Strategic Partnership for Edge Computing and IoT Security

NXP and Alibaba Cloud Announce Strategic Partnership for Edge Computing and IoT Security

NXP and Alibaba Cloud Announce Strategic Partnership for Edge Computing and IoT Security

NXP Semiconductors today announced a strategic partnership with Alibaba Cloud, the cloud computing and business unit of Alibaba Group.

The two companies are working together to enable development of secure smart devices for edge computing applications and have plans to further develop solutions for the Internet of Things (IoT).

As part of the partnership, AliOS Things, the Alibaba IoT operating system has been integrated onto NXP applications processors, microcontroller chips, and Layerscape multicore processors. Both NXP’s i.MX and Layerscape processors are currently the only embedded systems on the market using the Alibaba Cloud TEE OS platform. The new solution benefits various markets including automotive, smart retail and smart home. And it is currently being applied in applications such as automotive entertainment and infotainment systems, QR code payment scanning applications and smart home speakers.

Li Zheng, NXP global senior vice president and President of Greater China, said:

“As the leader of IoT innovation in China, Alibaba Cloud has launched a range of IoT basic and content services to support the demands of cloud computing, big data, AI [artificial intelligence], cloud integration and security. Alibaba Cloud IoT kit has launched more than 200 categories, with a total of more than 10 million sets of sales.”

“Our partnership with Alibaba Cloud will promote the continuous and steady expansion of NXP’s technological advantages for edge computing and IoT security, and will support the long-term and secure development of China’s IoT ecosystem.”

“We share the same vision as NXP on providing advanced and secure IoT solutions for an ‘everything connected’ world,” said Ku Wei, General Manager of IoT of Alibaba Cloud. “Based on the integration of AliOS Things with NXP’s applications processors and microcontroller chips, our comprehensive solution will better serve the development of China’s local commercial and manufacturing industries.”

With the deep partnership between NXP and Alibaba Cloud Link in the field of IoT security, NXP has become a council member of the ICA IoT Connectivity Alliance. In the future. The two companies plan to jointly develop solutions to support application development in different fields including smart manufacturing and smart city.

The ‘Annual Report of China IoT Development 2015-2016’ predicts that the amount of equipment connected to IoT globally will reach 20-50 billion by 2020, with 80 percent of that equipment in China. NXP’s robust product portfolio covers offering from the edge node to gateway and comprehensive cloud IoT solutions. NXP’s products are widely used in smart homes, smart cities, smart transportation, and secure connectivity.

In China, NXP combines outstanding enterprises in upstream and downstream industries, working together with industry leaders for the safe, connected, sustainable development and motivation for innovation of IoT.

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AT&T plans edge computing test zone for Silicon Valley

AT&T plans edge computing test zone for Silicon Valley

US telco AT&T plans facility where partners can run edge computing experiments in areas such as self-driving cars and augmented reality.

With edge computing now firmly finding its place in the IoT, US telco AT&T has laid down plans to open an edge computing test zone in the Bay Area of northern California in early 2018.

The zone itself is intended to be a cross between a proof of concept (PoC) lab and a developer hack shop. Initial reports suggest that AT&T will invite partners to test connected applications there, such as self-driving car software, drones and augmented and virtual reality (AR/VR) innovations.

At launch, the zone will use a 4G LTE connection, but the engineering team behind the lab zone hope to upgrade to 5G once the final standards and equipment are ready.

Read more: AT&T fires up LTE-M network in the US

The next step

“Edge computing is the next step in the evolution of the network,” claimed Melissa Arnoldi, president of AT&T Technology and Operations. “As [fast] connectivity becomes ubiquitous, it also needs to become smart. Edge computing puts a supercomputer in your pocket, on your wrist, in your car, and right in front of your eyes.”

The company has suggested that edge computing’s core challenge is striking the right balance between functionality or power. For example, today, an AR app running on a smartphone can offer high-end images or longer battery life, but not both. Cranking up the visual detail burns through the battery. Reducing power consumption generally means graphics that aren’t as sharp.

The answer, then, according to the company at least, is to move processing to the cloud as the next logical step.

Read more: AT&T commits to $ 200 million investment in IoT start-ups

Where cloud comes in

The cloud computing model of service-based application delivery and data storage, processing and analytics is widely agreed to be a logical step not just for edge computing, but for the majority of IT deployments. Where AT&T may be offering additional insight is in the expertise it can draw from its heritage in network transmission technologies.

The company says that in today’s networks, physical distances between users and data centers creates latency. As requests and responses travel hundreds or thousands of miles, users often notice the delay.

“With edge computing, we’ll install graphics processors and other computers in cell towers, small cells and other parts of our network that are never more than a few miles from our customers. This is what’s known as the edge of the network. In addition, low latency is being built into 5G from the get-go. The result: you will be able to run high-end applications in the cloud, and it will feel like it’s all happening right on your device,” said the company.

Read more: Elemental Machines uses AT&T IoT tech to make lab equipment smarter

An Agile approach

Developers and other third parties will be invited to test and innovate at AT&T’s Palo Alto-based edge computing and, as with all R&D work, success is never guaranteed. But the company says its rapid innovation model (as in, Agile with a capital A) means it can move on quickly when an approach isn’t panning out and apply lessons learned to future projects.

“Our goal in this experiment is to find the right architecture, the right services and the right business value in this ecosystem,” said Igal Elbaz, head of AT&T Foundry. “It’s all about moving quickly and collaborating closely with third-party innovators and developers.”

Read more: AT&T delivers progress report on LTE-M rollout in Mexico

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