June 2018

Written by Professor Yonghua Li with Beijing University of Posts and Telecommunications (BUPT) and active EdgeX Foundry and Zephyr Project member

The Beijing University of Posts and Telecommunications (BUPT) recently became new members of EdgeX Foundry and the Zephyr Project but students studied both projects long before then. In fact, students are currently working on IoT projects that are based on these open source technologies.

The first project uses the EdgeX platform (using the Java micro services) running on a Raspberry Pi. EdgeX Foundry core, supporting and export micro service artifacts were created in Eclipse using Java Maven.  Docker containers were created around the micro service artifacts and deployed to the Pi running Ubuntu 16.04 Linux using Docker Compose. In addition to the EdgeX export, core, and supporting micro services, the students choose to build and deploy an MQTT based device service to ingest sensor data into EdgeX which utilized CloudMQTT as the underlying broker.

For this project, the students used a random number generator to simulate sensor data on the Pi.  The simulated sensor data was passed in with an MQTT messages through the device service (again utilizing CloudMQTT) while receipt messages were sent back out from the device service through another MQTT pipe.  Because of CloudMQTT’s WebSocket user interface, students could easily view the JSON-wrapped random number data enter EdgeX as well as be acknowledged by EdgeX – demonstrating the successful establishment of an IoT edge platform.

EdgeX Foundry uses the random number as messages through the MQTT Microservice, passes it to CloudMQTT, and receives the response again through the MQTT Microservice. CloudMQTT’s WebSocket UI allows you to view JSON random number data and send data to EdgeX, demonstrating the successful establishment of it.

The other project aims to develop a Bluetooth-enabled heart rate monitor based on Zephyr OS, which is ideal for resource-constrained systems and small IoT devices. The system implements heart rate measurement for users and transmits the user’s heart rate data to user’s mobile phone via Bluetooth, so that users can monitor his or her heart rate in real-time.

The system is mainly divided into two parts: hardware and software. The main functions of the hardware is data collection, data transmission and data display. The hardware is designed and implemented centered on the Arduino101 development board. On the other hand, the software is mainly used for data conversion and analysis. The development and implementation of software are performed under Zephyr OS.

If you’d like to learn more about the BUPT student projects, we invite you to join the IoT Meetup on Tuesday, June 26 from 6-9 pm at VMware’s office in China. There is no cost for this event but space is limited, so RSVP is required. Register now https://www.bagevent.com/event/1491965   

If you have questions or comments, visit the EdgeX Rocket.Chat and share your thoughts in the #community channel.

Written by Jijun Ma, Member of the EdgeX Foundry Governing Board and Industrial Internet Director at Wanxiang Group

The Industrial Internet Consortium® (IIC) announced the Optimizing Manufacturing Processes by Artificial Intelligence (OMPAI) testbed yesterday. The OMPAI testbed is led by IIC members Wanxiang Group (also a member of EdgeX Foundry) and Thingswise and supported by Dell EMC (a founding member of EdgeX Foundry), Xilinx, China Unicom, and China Academy of Information and Communication Technology (CAICT).

The OMPAI testbed, which is the first testbed based on EdgeX edge computing platform, explores the application of artificial intelligence (AI) and industrial internet technologies, deployed from the edge to the cloud, to optimize automotive manufacturing processes. It also seeks to create an ecosystem that will foster the exchange of IT/AI/OT domain knowledge and the co-development of smart manufacturing applications. For example, deep learning may be able to improve quality assurance of an automobile part to substantially increase the detection of defects and reduce the need for manual inspection.

Vincent Wang, Chief Innovation Officer of Wanxiang Holdings, said, “As a leading multinational corporation in automotive and renewable energy, with factories in Europe, North America and Asia, we believe that an industrial IoT platform will be a key enabler for our digital transformation and global synergy. We are glad to work with technology leaders to validate AI, edge-cloud collaborative computers, and high-speed cellular networks to optimize manufacturing productivity and quality. This is the first step toward an open, inclusive IIoT platform on which we will continue with further testbeds, incorporating new ideas, new data usage models and creating greater value add. We invite worldwide enterprises, innovators and entrepreneurs to enrich the ecosystem together.”

In the edge platform, AI models and edge applications are run for the local optimization of manufacturing processes. In the cloud platform, they are run to enable global and long-term optimization, e.g. across production lines and plants. The edge platform also supports connectivity to and data collection from the equipment while the cloud enables historical data accumulation and storage and supports AI model building.

The cloud computing platform also provides the capability for enabling industrial app DevOps processes supporting collaboration between AI/IT developers and plant engineers in creating, testing and running data/AI model-driven industrial applications. The following image shows the solution overview of this testbed.

Blow are the usage scenarios in our testbed.

Machine vision on-line quality assurance

The main theme of this scenario is to exploit the capability of deep learning in image pattern recognition to improve quality assurance effectiveness and efficiency by increasing defect detection accuracy, reducing dependence on manual inspection and at the end providing online feedback to the production process to reduce defect rate.

Battery Cell Welding Quality Control

In this scenario, it is going to use historical data to analyze the relationship between the welding process and environmental parameters and the product quality and use that to predict in real time quality product and provide recommendation for optimization.

Wheel Bearing Production Line Balance & Optimization

A high throughput discrete manufacturing line usually consists of many workstations involving with various equipment and processes. These workstations may have different production throughput that vary depending on their process parameters. Mismatched throughput between the workstations would impede the overall production line throughput, reducing overall equipment utilization and production capacity.

Big data analytics on data collected from the workstation equipment can be used to monitor production pace of each of the workstations and overall throughput, and to identify bottlenecks and recommend optimization solutions.

Predictive Maintenance of grinding machines

In a manufacturing environment, equipment failures interrupt production lines or cause product quality issues, aggravated by the prevailing condition that few or no spare parts are usually kept for key equipment, e.g., grinding machines and motors, resulting severe reduction of production capacity in the event of equipment failures.

The current solution of “preventive maintenance” relying on periodic manual inspection is ineffective, laborious and interruptive to production. Predictive Maintenance for the equipment enabled by machine learning will be experimented within the general framework to effectively address this common manufacturing issue.

This testbed is open to new innovative ideas and EdgeX Foundry members are welcomed to join us to widely use EdgeX for industrial internet solution.

If you have questions or comments, visit the EdgeX Rocket.Chat and share your thoughts in the #community channel.

Written by Keith Steele, CEO of IOTech and EdgeX Foundry Board Member and the Chair of the Technical Steering Committee

Last week, we held a Face-to-Face Technical Steering Committee meeting in Palo Alto. It was another successful one and, after each meeting, my confidence grows that the EdgeX Foundry project will achieve great things.

Before reflecting on the week, I’d like to pass on my thanks on behalf of the community to VMware who hosted the event at their wonderful Palo Alto facility. California Burritos from their cool on-site restaurant was a culinary discovery for me!

EdgeX Foundry passed our one-year birthday in April, so from the EdgeX Charter standpoint, we now move from ‘start up’ phase to ‘steady state.’

While the term ‘steady state’ in the Project Charter refers to a transition to TSC members being voted from the contributing community, it’s a bit of a misnomer when you look at the project activity. This F2F TSC meeting demonstrated that EdgeX is far from static as it grew in attendance when compared to the last F2F meeting. More than 40 people showed up in person from all 4 corners of the world and many more joined by phone. We’re still very much in growth phase…

Elections were held for the TSC and we welcome new Working Group chairs Steve Osselton (Device Services, from IOTech), Trevor Conn (Core, from Dell) and David Ferriera (Security, from ForgeRock). Likewise, we thank Salim AbiEzzi, Doug Gardner and Tony Espy for their contributions on the TSC over the past year, all three will remain active participants in the project going forward.

So, on to the meeting…

The main discussion for the meeting was the status of the California Release, which is projected for early July and the roadmap for the Delhi release due in October.

Here’s a short list of what was scoped for the Delhi release:

• Device Service SDKs in Go and C will be previewed this summer and formally released with Delhi (including some representative device services).
• Performance targets for EdgeX are already being hit, but performance testing as part of the continuous integration and release process will be incorporated.
• Support for binary data to be processed by EdgeX for the first time to allow for carrying video images, audio data, and the like.
• The initial system management functionality will include an API for the management of the micro services and an agent to coordinate with other application/cloud infrastructure.
• Refactored services to incorporate better isolation to allow for future replacement of infrastructure elements such as the local persistent store or message infrastructure.
• The addition of an EdgeX UI which will be previewed this summer but officially released with Delhi.
• Research and design on a new Application Services layer to replace the existing Export Services layer of EdgeX will be published with plans to have implemented with the Edinburgh release (scheduled for April 2019).

Research and recommendations on options for the placement of MongoDB as the included reference database will also be announced with Delhi with the intention of offering changes by Edinburgh release.

I was really impressed with the level of collaboration and cooperation at the meeting as there was fabulous participation from all. If you couldn’t make it, this is a timely reminder that EdgeX Foundry is an open project and the recordings of the meeting can be found here.

One thing we did at this meeting, which I thought worked well, was we held a separate Face-to-Face meeting for the Device Working Group prior to the main meeting. Doing this enabled much deeper technical collaboration on important issues before the main meeting. I think this is something we should formalize into our meeting structure across all groups in future meetings.

Additionally, Samsung sought support for updating the project positioning and received it from the TSC. The suggestion is to avoid branding that suggests EdgeX as a strictly industrial IoT platform, especially since EdgeX can be used in much broader IoT solutions to include enterprise, consumer and mobile edge environments. While we will continue to strive to make the platform suitable for industrial workloads, the project will begin to ensure EdgeX Foundry marketing and literature addresses its broader capabilities.

This will certainly lead to a growth in the scope of the Vertical Working Group activity but market positioning was referred to the EdgeX Marketing group with TSC input provided.

The next F2F TSC meeting will be held in my home town of Edinburgh on October 23-24. You can register here and find more information available on the EdgeX Wiki.

We look forward to collaborating with you there!!

Best Regards,

Keith Steele, TSC Chair

If you have questions or comments, visit the EdgeX Rocket.Chat and share your thoughts in the #community channel.

Written by Gorka Garcia, Active Contributor in the EdgeX Community and Senior Lead Engineer at Cavium Inc.

Cavium joined EdgeX Foundry last year and has been committed to get full support for ARM64 in EdgeX, as we explained in our previous blog post. One common drawback of many open source projects is the lack of both build and test in ARM platforms in their Continuous Integration systems (CI systems). This issue can affect customers – it takes time and effort from their engineering resources to work with open source projects and integrate their platform of choice. This directly affects time to market.

On March 1, the Cavium team reached a very important milestone in the process of having ARM64 support in EdgeX Foundry. We got our first EdgeX ARM64 native build and test in the CI system! Since March 1, this machine has performed more than 700 builds with their corresponding unit tests.

The Linux Foundation, which is responsible for the CI system, helped by running it on an OcteonTX platform in Cavium premises and integrating this OcteonTX platform as a build executor node in Jenkins, the CI system. With their help and comparing what was done for PC, we managed to install all the dependencies and had it working in a short time. Since March 1, this machine has performed 26 build works and there have been 141 snapshots of the ARM images built total.

Moving forward, the EdgeX community will be notified of any changes on the source code that affects ARM64 compilation and testing. The next step in this process will be getting CI system to also perform black box testing in the same platform.

Additionally, Cavium recently announced support for EdgeX on its OCTEON TX® family of products, including the CN80xx/81xx and the CN83xx series. Click here for more details.

For more information:

• https://wiki.edgexfoundry.org/display/FA/EdgeX+Foundry+Project+Wiki
• https://nexus.edgexfoundry.org/content/sites/edgex-go/
• https://nexus3.edgexfoundry.org/#browse/browse:docker.staging

If you have questions or comments, visit the EdgeX Rocket.Chat and share your thoughts in the #community channel.