Tech News - Embedded Systems

Infosys recently announced its partnership with Siemens to build applications for Siemens' open cloud-based IoT operating system, Mindsphere. Mindsphere connects real-world objects (industrial machinery, systems, equipments and so on) to the digital world with the help of IoT using advanced analytics. It provides industry applications and services to help businesses achieve success. With this collaboration, Infosys and Siemens will enable customers to leverage the true power of data generated by their devices. The initial focus as company plans will be on customers in the manufacturing, energy, utilities, healthcare, pharmaceutical, transportation, and logistics industry. How Infosys plans to help Siemen’s Mindsphere: Infosys plans to offer end-to-end implementation services and post-implementation support for Mindsphere It will be using its repository of Industry 4.0 accelerators, platform tools, etc. to help customers get quickly on board Will enhance customers to have an efficient experience by using data analytics features such as predictive maintenance and end-to-end factory visibility Customers will also benefit by monetizing new data-driven services Ravi Kumar S, President and Deputy COO, Infosys, says, “There is an increasing need for enterprises to accelerate their digital journey and to deliver new and innovative services. This partnership will help us bring exciting solutions to our customers that will combine strategic insights and execution excellence.” With Infosys’ expertise in the field of industrial engineering, industrial analytics, AR and VR and with Siemens’ strength in manufacturing industrial assets brings valuable digital services to customers from different sectors. Know more about the partnership alliance on the Infosys Blog post. 5 DIY IoT projects you can build under $50 Build an IoT application with Google Cloud [Tutorial] Google releases Android Things library for Google Cloud IoT Core

Google is the new platinum member of the Linux Foundation. Google will benefit the platinum member rights and the Linux community will move towards huge financial gains. The annual membership cost for Google will be around $500,000. Linux Foundation, on the other hand, is quite thrilled to have Google as one of the platinum members.  As Google is one of the biggest contributors to and supporters of open source in the tech world. In addition to this, Google leverages one of the most important open source projects for its OS -- the Linux kernel and both Android and Chrome OS, are Linux-based. This membership also secured a seat for Sarah Novotny, Google’s Head of Open-Source strategy for the Google Cloud Platform into the Board of Directors of Linux Foundation. On this achievement, Sarah mentioned, ’Open source is an essential part of Google's culture, and we've long recognized the potential of open ecosystems to grow quickly, be more resilient and adaptable in the face of change, and create better software. The Linux Foundation is a fixture in the open source community. By working closely with the organization, we can better engage with the community-at-large and continue to build a more inclusive ecosystem where everyone can benefit.’ Google joins hands with other platinum members in the Linux Foundation, including Microsoft, Intel, Huawei, Samsung, Facebook, etc. Read more about this exciting coverage at the Linux Foundation’s official announcement. Tencent becomes a platinum member of the Linux Foundation Machine learning APIs for Google Cloud Platform Google introduces Machine Learning courses for AI beginners

Microsoft recently announced Azure IoT Edge to be generally available and open source. Its preview was announced at the Microsoft Build 2017, during which the company stated how this service plans to extend cloud intelligence to edge devices. Microsoft Azure IoT Edge is a fully-managed cloud service to help enterprises generate useful insights from the data collected by the Internet of things (IoT) devices. It enables one to deploy and run Artificial Intelligence services, Azure services, and custom logic directly on the cross-platform IoT devices. This, in turn, helps deliver cloud intelligence locally as per the plan. Additional features in the Azure IoT Edge include: Support for Moby container management system: Docker which is built on Moby, an open-source platform. It allows Microsoft Azure to extend the concepts of containerization, isolation, and management from the cloud to devices at the edge. Azure IoT Device Provisioning Service: This service allows customers to securely provision huge amount of devices making edge deployments more scalable. Tooling for VSCode: VSCode allows easy module development by coding, testing, debugging, and deploying. Azure IoT Edge security manager: IoT Edge security manager acts as a tough security core for protecting the IoT Edge device and all its components by abstracting the secure silicon hardware. Automatic Device Management (ADM): ADM service allows scaled deployment of IoT Edge modules to a fleet of devices based on device metadata. When a device with the right metadata (tags) joins the fleet, ADM brings down the right modules and puts the edge device in the correct state. CI/CD pipeline with VSTS : This allows managing the complete lifecycle of the Azure IoT Edge modules from development, testing, staging, and final deployment. Broad language support for module SDKs: Azure IoT Edge supports more languages than other edge offerings in the market. It includes C#, C, Node.js, Python, and Java allowing one to program the edge modules in their choice of language. There are three components required for Azure IoT Edge deployment: Azure IoT Edge Runtime Azure IoT Hub and Edge modules. The Azure IoT Edge runtime is free and will be available as open source code. Customers would require an Azure IoT Hub instance for edge device management and deployment if they are not using one for their IoT solution already. Read full news coverage at the Microsoft Azure IoT blog post. Read Next Microsoft commits $5 billion to IoT projects Epicor partners with Microsoft Azure to adopt Cloud ERP Introduction to IOT

Kontakt.io, a leading IoT location platform provider, recently announced partnership with the IOTA Foundation, a non-profit open-source foundation which backs IOTA. This partnership aims at integrating IOTA’s next generation distributed ledger technology to Kontakt.io’s location platform and is designed specifically for condition monitoring and asset tracking. The integration will allow a tamper-proof and chargeable readings of smart sensor data. It is beneficial for healthcare operators and supply chain firms, which monitor environmental conditions for compliance reasons. They can explore fully transparent ways for storing and reporting on telemetry data. Kontakt.io’s IoT platform and IOTA’s Blockchain partnership will encrypt device-to-device and device-to-cloud communication of telemetry so the data remains intact. Consumers which include manufacturers, carriers, inspectors, technology providers, and others can leverage this new technology as it would: Increase trust and transparency Ease dispute resolution Result in better compliance breach detection and Prevent delivery of faulty products How Kontakt.io and IOTA benefit each other IOTA eliminates the cost barrier, and needs lesser computing power to confirm transactions. Unlike Blockchain or Ethereum, IOTA is capable of processing a lot of operations in real time. It scales faster depending on the amount of transactions it has queued. Hence, Proof of Work(PoW) is now possible and efficient in the IoT environment with IOTA. It is likely to become the next security standard for IoT. On the other hand, IOTA has partnered with Kontakt.io to empower the building blocks of a smart supply chain using the powerful IoT platform. Read more about this partnership at Kontakt.io official website. How to run and configure an IoT Gateway Build your first Raspberry Pi project 5 reasons to choose AWS IoT Core for your next IoT project

Internet of Things has shown a remarkable improvement over the years. The basic IoT embedded devices with sensors, have now advanced to a level where AI can be deployed into IoT devices to make them smarter. The IoT World 2018 conference was held from May 14th to 17th, at Santa Clara Convention Center, CA, USA. Al special developer centric conference designed  specifically for technologists was also part of the larger conference. The agenda for the developers’ conference was to bring together the technical leaders who have contributed with their innovations in the IoT market and tech enthusiasts who look forward to develop their careers in this domain.This conference also included sessions such as SAP learning, and interesting keynotes on Intelligent Internet of things. Here are five sessions that caught our eyes at the developer conference in IoT World 2018. How to develop Embedded Systems by using the modern software practices, Kimberly Clavin Kimberly Clavin  highlighted that a major challenge in developing autonomous vehicles include, system integration and validation techniques. These techniques are used to ensure quality factor within the code. There are a plethora of companies that have software as their core and use modern software practices such as (Test Driven Development)TDD and Continuous Integration(CI) for successful development. However, the same tactics cannot be directly implemented within the embedded environment. Kimberly presented ways to adapt these modern software practices for use within the development of embedded systems. This can help developers to create systems that are fast, scalable, and a cheaper. The highlights of this session include, Learning to test drive an embedded component. Understanding how to mock out/simulate an unavailable component. Application of Test Driven Development (TDD), Continuous Integration (CI) and mocking for achieving a scalable software process on an embedded project. How to use Machine Learning to Drive Intelligence at the Edge, Dave Shuman and Vito De Gaetano Edge IoT is gaining a lot of traction of late. One way to make edge intelligent is by building the ML models on cloud and pushing the learning and the models onto the edge. This presentation session by Dave Shuman and Vito De Gaetano, shows how organizations can push intelligence to the edge via an end-to-end open source architecture for IoT. This end-to-end open source architecture for IoT is purely based on Eclipse Kura and Eclipse Kapua. Eclipse Kura is an open source stack for gateways and the edge, whereas Eclipse Kapua is an open source IoT cloud platform. The architecture can enable: Securely connect and manage millions of distributed IoT devices and gateways Machine learning and analytics capabilities with intelligence and analytics at the edge A centralized data management and analytics platform with the ability to build or refine machine learning models and push these out to the edge Application development, deployment and integration services The presentation also showcased an Industry 4.0 demo, which highlighted how to ingest, process, analyze data coming from factory floors, i.e from the equipments and how to enable machine learning on the edge using this data. How to build Complex Things in a simplified manner, Ming Zhang Ming Zhang put forth a simple question,“Why is making hardware so hard?” Some reasons could be: The total time and cost to launch a differentiated product is prohibitively high because of expensive and iterative design, manufacturing and testing. System form factors aren’t flexible -- connected things require richer features and/or smaller sizes. There’s unnecessary complexity in the manufacturing and component supply chain. Designing a hardware is a time-consuming process, which is cumbersome and not a fun task for designers, unlike software development. Ming Zhang showcased a solution, which is ‘The zGlue ZiPlet Store’ -- a unique platform wherein users can build complex things with an ease. The zGlue Integrated Platform (ZiP) simplifies the process of designing and manufacturing devices for IoT systems and provides a seamless integration of both hardware and software on a modular platform. Building IoT Cloud Applications at Scale with Microservices, Dave Chen This presentation by Dave Chen includes how DConnectivity, big data, and analytics are transforming several business types. A major challenge in the IIoT sector is the accumulation of humongous data. This data is generated by machineries and industrial equipments such as wind turbines, sensors, and so on. Valuable information out of this data has to be extracted securely, efficiently and quickly. The presentation focused on how one can leverage microservice design principles and other open source platforms for building an effective IoT device management solution in a microservice oriented architecture. By doing this, managing the large population of IoT devices securely becomes easy and scalable. Design Patterns/Architecture Maps for IoT Design patterns are the building blocks of architecture and enable developers and architects to reuse solutions to common problems. The presentation showcased how various common design patterns for connected things, common use cases and infrastructure, can accelerate the development of connected device. Extending Security to Low Complexity IoT Endpoint Devices, Francois Le At present, there are millions of low compute, low power IoT sensors and devices deployed. These devices and sensors are predicted to multiply to billions within a decade. However, these devices do not have any kind of security even though they hold such crucial, real-time information. These low complexity devices have: Very limited onboard processing power, Less memory and battery capacity, and are typically very low cost. Low complexity IoT devices cannot work similar to IoT edge device, which can easily handle validation and encryption techniques, and also have huge processing power to handle multiple message exchanges used for authentication. The presentation states that a new security scheme needs to be designed from the ground up. It must acquire lesser space on the processor, and also have a low impact on battery life and cost. The solution should be: IoT platform agnostic and Easy to implement by IoT vendors, Easily operated over any wireless technologies (e,g, Zigbee, BLE, LoRA, etc.) seamlessly Transparent to the existing network implementation. Automated and scalable for very high volumes, Evolve with new security and encryption techniques being released Last for a long time in the field with no necessity to update the edge devices with security patches. Apart from these, many other presentations were showcased at the IoT World 2018 for developers. Some of them include, Minimize Cybersecurity Risks in the Development of IoT Solutions, Internet of Things (IoT) Edge Analytics: Do's and Don’ts. Read more keynotes presented at this exciting IoT World conference 2018 on their official website. Cognitive IoT: How Artificial Intelligence is remoulding Industrial and Consumer IoT How IoT is going to change tech teams AWS Greengrass brings machine learning to the edge    

Splunk has announced Splunk Industrial Asset Intelligence (Splunk IAI). It's now available on limited release. It would be made open to general availability later this year, in the fall. What is Splunk Industrial Asset Intelligence? The Splunk IAI system makes it easy for customers to use Splunk’s data analytics capabilities for analyzing data from industrial systems and devices. Critical industrial systems lack real time visibility. This can lead to a reactive approach to managing industrial operations and problems are often solved via intuition instead of a data-driven approach. Splunk Industrial Asset Intelligence (SIAI) has been introduced to combat these challenges facing companies in manufacturing, energy, transportation, oil and gas and other industrial verticals. The SIAI would be built on top of Splunk Enterprise machine data platform. The benefits of Splunk Industrial Asset Intelligence Benefits of Splunk IAI include : It correlates data from Industrial Control Systems (ICS), sensors, SCADA systems and applications, making it easy to monitor and diagnose equipment and operational issues in real time. Enables customers to respond to issues faster without affecting production, where unplanned downtime can equate to millions of dollars in lost revenue. The packaged set of capabilities provided by Splunk Industrial Asset Intelligence easily integrates with the existing Splunk platform. The Splunk IAI offers a single solution that ensures industrial systems running at full capacity, enabling organisations to significantly save resources and money on unplanned downtime. To learn more about Splunk Industrial Asset Intelligence, visit Splunk’s website.

AWS already has solutions for machine learning, edge computing, and IoT. But a recent update to AWS Greengrass has combined all of these facets so you can deploy machine learning models to the edge of networks. That's an important step forward in the IoT space for AWS. With Microsoft also recently announcing a $5 billion investment in IoT projects over the next 4 years, by extending the capability of AWS Greengrass, the AWS team are making sure they set the pace in the industry. Jeff Barr, AWS evangelist, explained the idea in a post on the AWS blog: "...You can now perform Machine Learning inference at the edge using AWS Greengrass. This allows you to use the power of the AWS cloud (including fast, powerful instances equipped with GPUs) to build, train, and test your ML models before deploying them to small, low-powered, intermittently-connected IoT devices running in those factories, vehicles, mines, fields..." Industrial applications of machine learning inference Machine learning inference is bringing lots of advantages to industry and agriculture. For example: In farming, edge-enabled machine learning systems will be able to monitor crops using image recognition  - in turn this will enable corrective action to be taken, allowing farmers to optimize yields. In manufacturing, machine learning inference at the edge should improve operational efficiency by making it easier to spot faults before they occur. For example, by monitoring vibrations or noise levels, Barr explains, you'll be able to identify faulty or failing machines before they actually break. Running this on AWS greengrass offers a number of advantages over running machine learning models and processing data locally - it means you can run complex models without draining your computing resources. Read more in detail on the AWS Greengrass Developer Guide. AWS Greengrass should simplify machine learning inference One of the fundamental benefits of using AWS Greengrass should be that it simplifies machine learning inference at every single stage of the typical machine learning workflow. From building and deploying machine learning models, to developing inference applications that can be launched locally within an IoT network, it should, in theory, make the advantages of machine learning inference more accessible to more people. It will be interesting to see how this new feature is applied by IoT engineers over the next year or so. But it will also be interesting to see if this has any impact on the wider battle for the future of Industrial IoT. Further reading: What is edge computing? AWS IoT Analytics: The easiest way to run analytics on IoT data, Amazon says What you need to know about IoT product development