Home Edge—an open source edge computing framework for home devices—is now available in its Eucalyptus (vE) release. Home Edge, an LF Edge project, is a robust, reliable and intelligent home edge computing open source framework and ecosystem running on a variety of devices in the home. To accelerate the deployment of the edge computing services ecosystem successfully, the Home Edge Project provides users with an interoperable, flexible, and scalable edge computing services platform with a set of APIs that can also run with libraries and runtimes.
“I am excited to share the availability of vE version of Home Edge on Github,” said Suresh L C, Home Edge Technical Steering Committee (TSC) Co-Chair. “The new Home Edge release adds Android execution support with a secure, robust and end-to-end framework for intelligent service offloading in smart home scenarios, which in turn provides data privacy with low latency response.”
The vE release adds a number of significant new features, among them are
Open Source Security Foundation (OpenSSF) Badge
Home Edge has achieved the OpenSSF best practices Gold badge and the OpenSSF scorecard has also been integrated. Go-Project is made compliant by incorporating necessary documentation/quality fixes.
Data synchronization to Cloud endpoints – MQTT
Independent MQTT based cloud synchronization mechanism as enhancement to vD
API’s to send and receive data from and to between Service application and Cloud via Home Edge
MQTT broker would be configured at the cloud (Cloud agnostic)
Home Edge acts as MQTT client to exchange data
TLS secure mode for all the data exchange
Platform enhancement
The support for the execution of Home Edge on Android has been added to Home Edge Android
The base code currently supports device/service discovery and service offload to Linux device
Service offload to Android from Linux is not supported in this release
Dependent bot integration
Bot to check on the updates on dependent libraries has been integrated
The bot has been configured to run once every month in the first week
Code enhancements
Fixed TxT Record parsing as per mDNS protocol
Refactored service list assignment logic TxT record
Modified get score API from GET to POST in line with API design rules
API to add/delete events from database based on event ID added for DataStorage
Code coverage increased by adding more test cases
Modified response for Ping request to Pong
Auto numerical tagging of code when significant changes are incorporated
Learn more about these and other features of Home Edge vE release in the release notes.
What’s next
For the next steps, the community will work on strengthening the Android version of Home Edge so that the features are in line with the Go version.
If you are using or evaluating Home Edge, please let the project TSC know and join the TSC meetings. Your feedback on the project is greatly appreciated!
EdgeX Foundry now fully embraces decoupled message bus communication within the platform
I am delighted to announce today the availability of the EdgeX Foundry 2.3 release which is codenamed Levski. This is the project’s 11th official release since its inception in 2017 and includes some significant new features and benefits to users that I’ll explain in this blog.
Having been elected as the chair of the EdgeX Technical Steering Committee (TSC) earlier this summer, it’s great to be able to announce the news of this release but it really is the technical expertise and dedication of the EdgeX development team that helps bring these releases to the wider community. Thanks again for everyone’s effort here. Great to be part of a group of many different companies and individuals working together to make great open source edge software.
What is Levski?
First let me give mention to the Levski codename. As you may know, each EdgeX release is named after a certain place or location in the world, with the specific place chosen each time significant contributors to the project. This version was named by two long term EdgeX contributors, Diana Atanasova and Malini B
handaru, both from VMware. For version 2.3 and the letter “L”, we come back over to Europe because Levski is a large mountain in the beautiful European country of Bulgaria. Bulgaria is also Diana’s homeland so it’s nice to be able to recognize that with the naming of this release.
New in EdgeX 2.3 – More about the Message Bus
Perhaps the biggest single new feature is the enhancement to support the delivery of commands via the EdgeX message bus.
We’ve previously made great strides in EdgeX V2 by delivering data from the southside (from devices and sensors) to the northside (to the core and application layers) via an internal message bus. This release also adds the support in the other direction, i.e., from the northside (the application layers and core layers) down to the southside (the devices and sensors) on the same message bus. Previously the southbound communication was exclusively via REST. Moving to support message bus-based communication in both directions is a big advancement and brings key benefits in terms of reduced latency and increased scalability. The asynchronous communication and the QoS-based control that you get from the message bus implementations that you can choose adds delivery guarantees and retransmissions of messages as needed.
Message Bus
More Run-Time Data
Another key area of development for Levski has been the focus on providing EdgeX users with more live information about how the system is running. EdgeX 2.3 adds System or “Control Plane” Events that can provide live updates as to what is happening. For example, users can receive notifications that new devices are added or that there has been a network disconnection from a specific device.
Somewhat related to System Events are the Telemetry Metrics. In contrast to events though, the metrics provide numeric information relating to how the platform is operating. Examples include the number of data readings that are persisted by core data or the number of secrets or tokens that are stored. Building of what was delivered previously, Levski adds more metrics across all of the services.
All of this information can be collected and reported however the user sees fit. In addition, the System Events and Telemetry Metrics mechanisms are available for users to add their own metrics as needed.
Many other updates and enhancements
There are many other new additions in this last release cycle including:
Availability of NATS as alternative implementation of the internal EdgeX Message Bus. NATS is a popular and lightweight protocol with native delivery of EdgeX messages and potential advantages in high availability (HA) use cases
Authenticated access to the MQTT message bus
Securing of the Consul registry service with access tokens
Passing status at the Open Source Security Foundation (OpenSSF)
Initial construction of the EdgeX STRIDE threat model
Improving the EdgeX development process with a new Use Case Requirements (UCRs) phase in the design procedure
See the release notes here for full details of what the Levski release provides.
New adopters and use cases for EdgeX
Running as a mature, stage 3 Linux Foundation project, there are now many users and adopters of EdgeX technology around the world. That includes users who download and deploy the open source EdgeX code, but also users of commercial products (including ours at IOTech) that are based on EdgeX. Take a look at some of the companies who are users of EdgeX. There are also a good set of presentation videos from different adopters of the technology.
One of my personal aims as TSC chair is to help encourage the adoption and wider use of EdgeX. Thanks to Building System Integrators (BSI) who recorded a talk at a recent TSC meeting. It would be great to hear from more companies who would like to do something similar. Please do reach out to me to arrange it.
What’s coming next?
Each release marks a busy period where of course we finalize the current version, but already we are looking forward at what we can achieve in the next release. Codenamed Minnesota and expected to be version 3.0, we are looking to add new features to help with the scalable configuration of EdgeX. Tune into the Minnesota technical planning conference that we are running next week where we will scope out what we can do. As always, attendance and contributions are very welcome.
Thanks again to everyone who had a hand in this release, and we look forward to more successful releases in the future.
eKuiper—a lightweight IoT data analytics and streaming software—is now available in its 1.7.0 release. eKuiper, an LF Edge project, migrates real-time cloud streaming analytics frameworks such as Apache Spark, Apache Storm and Apache Flink to the edge. eKuiper references these cloud streaming frameworks, incorporates any special requirements of edge analytics and introduces rule engine, which is based on Source, SQL (business logic) and Sink; rule engine is used for developing streaming applications at the edge.
eKuiper 1.7.0 continues to improve the expressiveness of the rules by providing the lookup table, updatable sink, and more SQL syntax and functions. The 1.7.0 release adds a number of significant new features, among them are
Stream batch combined computation
Not all data changes frequently, even in real-time computation. In some cases, you may need to supplement stream data with externally stored static data. For example, user metadata may be stored in a relational database, and the stream data only has data that changes in real time, requiring a connection between the stream data and the batch data in the database to enrich the data. In the new version, eKuiper adds the new Lookup Table concept for binding external static data, which can be joined with stream data in the rules to realize the operation of stream-batch combination. Additionally, with the support of updatable sink, eKuiper can process CDC data and update the batch storage.
Enhanced analysis capabilities
Analytic functions refer to functions that perform state-related analysis by saving state, allowing users to complete some of the common stateful calculations without complex time windows or custom functions. In the new version, we have added the Partition By syntax to the analytic functions, allowing users to perform partitioned stateful calculations based on the dimensions defined by Partition By clause. We also added a new analysis function latest to get the latest value of data. It is used to collect unstructured data whose columns are not fixed and can be automatically stitched to calculate the complete data.
Expanding the connectivity ecosystem
On one hand, we continue to add built-in and extended source/sink by adding Httppush source, which allows users to push data to eKuiper via HTTP protocol; adding built-in Redis lookup source, which supports using Redis as external lookup table; adding Influx V2 sink, which supports writing to InfluxDB 2.x version. At the same time, Memory, SQL, and Redis source are adapted to support lookup tables; Memory, SQL, and Redis sink are adapted to be updatable.
Enhance the Ops experience
The new release refactors the external connection configuration API to enable easier connection resource management. Another major update comes from the bulk import/export and initialization of streams and rules to facilitate the migration of eKuiper instances.
Learn more about these and other features of eKuiper’s 1.7.0 release in the release notes.
What’s next
In the next release, the community will work on enhancing graph API to support more nodes, such as switch node and script node, to provide a more powerful and easy-to-use rule pipeline composing tool. A new function to run tensor flow lite model will be provided to easily integrate with AI models. Check out the evolving Github 1.8.0 milestones and we welcome you to add your wisdom.
The Internet of Things (IoT) market has expanded significantly in recent years. According to Gartner, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud by 2025. As edge computing becomes a significant revenue opportunity for the technology and telecom industries, it’s even more important to have effective leaders to help advance the future of edge computing.
Today we sat down with Daniel Lazaro, LF Edge Technical Advisory Council (TAC) vice-chair and Senior Technical Program Manager at AVEVA. Daniel tells us how he got involved in the edge computing industry, the LF Edge project umbrella, and why leaders must plan for the growth of IoT and edge.
How did you get involved in the LF Edge community and what is your role now?
In January 2019, OSIsoft, which was my employer at the time, joined LF Edge. My first official community role was with the LF Edge Technical Advisory Council (TAC) where I became the TAC representative for project Fledge, with the goal to drive community growth. As part of the TAC, we developed the LF Edge project lifecycle process and took in Fledge as a “Stage 1” project;” Fledge was actually the first project to go through the various maturity stages within LF Edge, beginning at project inception (Fledge is now “Stage 2” and in the process of becoming a “Stage 3” or “mature” project).
Later, I joined the Board and the Strategic Planning Committee to help drive the direction and growth of LF Edge as a whole. I have participated in various other efforts including SIGs, LF Edge lab, and the Outreach Committee to name a few. I was recently honored to be elected as TAC vice-chair and I look forward to collaborating even more across the LF Edge ecosystem.
What is your vision for the edge computing industry?
I believe edge computing is now at the point where the cloud was a decade ago: It is about to explode. Several forecasts estimate the size of the edge computing market to reach 156 billion by 2030. Gardner predicts that ”By 2025, more than 50% of enterprise-managed data will be created and processed outside the data center or cloud.” The time to catch the edge wave is now.
What impact do you see open source playing in the evolution of the edge market? And how has it shaped where we are today?
Open source is a platform for innovation, and as such, is helping to accelerate the development of edge solutions. Open source has enabled end users, developers and organizations to build communities that collaborate on projects that address current and future needs. Beyond LF Edge, I am also a voting member of FledgePOWER’s TAC, under LF Energy, a sister project and community that focuses on power built on top of Fledge. OSDU is another example of a community built around open source for the oil & gas vertical, also leveraging Fledge.
Why is LF Edge important to advance the future of edge computing?
LF Edge promotes sustainable ecosystems and communities of peers that fosters cross-industry collaboration, enables organizations to speed up adoption and delivers value to end users. LF edge is a neutral home for code and collaboration built on trust that focuses on edge computing projects.
What is AVEVA’s role in edge computing and LF Edge?
AVEVA, a leader in industrial software, is helping to drive the growth of the Fledge technology that was initially contributed by fellow collaborators and member organization Dianomic. AVEVA is also currently working on a reseller agreement with Dianomic with Fledge at its core.
The AVEVA Edge solution offers SCADA, HMI and IoT Edge solutions for OEMs, System Integrators, and end users with a focus on interoperability, mobility, and portability that runs on Linux. The PI Edge technology collects real-time data from remote assets and IIoT devices for intelligence that spans the entire operation.
What advice do you give to organizations who want to get involved in the LF Edge community?
Participate and contribute. Contributions come in many different shapes. Network with your peers, lead by example and let your voice be heard. At LF Edge, you will find a community ready to support and mentor you in your journey. Come join us!
About LF Edge:
LF Edge is an umbrella organization to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system. With the support of 28 Premier members, 28 General members and 14 associate members, LF Edge hosts 11 projects including Akraino, EdgeX Foundry, Project EVE, Fledge and more. Advance the future of edge computing with LF Edge and become a paying member.
Fledge—the open source Industrial IoT system—is now available in its 2.0 release. Fledge, an LF Edge project, is an open source framework and community for the industrial edge focused on critical operations, predictive maintenance, situational awareness and safety. Fledge is architected to integrate Industrial Internet of Things (IIoT), sensors and modern machines with the cloud and existing “brown field” systems like historians, Distributed Control Systems (DCS), Program Logic Controllers (PLC) and Supervisory Control and Data Acquisition (SCADA). All share a common set of administration and application APIs.
“I am extremely happy to announce the general availability of the Fledge 2.0 release”, said Mark Riddoch, Fledge TSC Co-chair, “It’s exciting to see the new release provides a scalable, secure, robust infrastructure for collecting data from sensors, processing data at the edge using intelligent data pipelines and transporting data to historian and other management systems.”
The 2.0 release adds a number of significant new features, among them are
Support for enhanced data types including vector data and images allowing computer vision techniques to be used in sensor data collection.
The introduction of a control flow allowing Fledge to be used not just to gather sensor data but to allow non-time critical control features.
A set of new features targeted to help both no-code data pipeline developers and developers of new plugins.
Expanded and restructured documentation including a best practices guide for developing data pipelines.
The notification and alerting system have been enhanced to allow for watchdog monitors to be added to the data flows into Fledge.
A number of new plugins have been added to the ever growing list of those available within Fledge.
A myriad of other new features, enhancements and bug fixes are also included, full details can be found in the release notes available as part of the online documentation.
In case you missed it, the ONE Summit agenda is now live! With 70+ sessions delivered by speakers from over 50 organizations, at ONE Summit, you can meet industry experts who will share their edge computing knowledge across 5G, factory floor, Smart Home, Robotics, government, Metaverse, and VR use cases, using LF Edge projects including Akraino, EdgeX Foundry, EVE and more.
Save your seat for the ONE Summit today and add these edge sessions to your schedule. We hope to see you in Seattle, WA November 15-16!
Frank Brockners, Distinguished Engineer, Cisco Sign.
Hurry! Early Bird (Corporate) registration closes September 9! Bookmark the ONE Summit website to easily find updates as more event news is announced, and follow LF Edge on Twitter to hear more about the event. We hope to see you in Seattle soon!
Hey everyone!
I am Ruchi Pakhle currently pursuing my Bachelor’s in Computer Engineering from MGM’s College of Engineering & Technology. I am a passionate developer and an open-source enthusiast. I recently graduated from LFX Mentorship Program. In this blog post, I will share my experience of contributing to Open Horizon, a platform for deploying container-based workloads and related machine learning models to compute nodes/clusters on edge.
Background
I have been an active contributor to open-source projects via different programs like GirlScript Summer of Code, Script Winter of Code & so on.. through these programs I contributed to different beginner-level open-source projects. After almost doing this for a year, I contributed to different organizations for different projects including documentation and code. On a very random morning applications for LFX were opened up and I saw various posts on LinkedIn among that posts one post was of my very dear friend, Unnati Chhabra, she had just graduated from the program and hence I went ahead and checked the organization that was a fit as per my skill set and decided to give it a shot.
Why did I apply to Open Horizon?
I was very interested in DevOps and Cloud Native technologies and I wanted to get started with them but have been procrastinating a lot and did not know how to pave my path ahead. I was constantly looking for opportunities that I can get my hands on. And as Open Horizon works exactly on DevOps and Cloud Native technologies, I straight away applied to their project and they had two slots open for the spring cohort. I joined their element channel and started becoming active by contributing to the project, engaging with the community, and also started to read more about the architecture and tried to understand it well by referring to their youtube videos. You can contribute to Open Horizon here.
Application process
Linux Foundation opens LFX mentorship applications thrice a year: one in spring, one in summer, and the winter cohort, each cohort being for a span of 3 months. I applied to the winter cohort for which the applications opened up around February 2022 and I submitted my application on 4th February 2022 for the Open Horizon Project. I remember there were three documents mandatory for submitting the application:
1. Updated Resume/CV
2. Cover Letter
(this is very very important in terms of your selection so cover everything in your cover letter and maybe add links to your projects, achievements, or wherever you think they can add great value)
The cover letter should cover these points primarily👇
How did you find out about our mentorship program?
Why are you interested in this program?
What experience and knowledge/skills do you have that are applicable to this program?
What do you hope to get out of this mentorship experience?
3. A permission document from your university stating they have no obligation over the entire span of the mentorship was also required(this depends on org to org and may not be asked as well)
Selection Mail
The LFX acceptance mail was a major achievement for me as at that period of time I was constantly getting rejections and I had absolutely no idea about how things were gonna work out for me. I was constantly doubting myself and hence this mail not only boosted my confidence but also gave me a ray of hope of achieving things by working hard towards it consistently. A major thanks to my mentors, Joe Pearson and Troy Fine, for believing in me and giving me this opportunity.⭐
My Mentorship Journey
Starting off from the day I applied to the LFX until getting selected as an LFX Mentee and working successfully for over 3 months and a half, it felt surreal. I have been contributing to open-source projects and organizations before. But being a part of LFX gave me such a huge learning curve and a sense of credibility and ownership that I got here wouldn’t have gotten anywhere else.
I still remember setting up the mgmt-hub all-in-one script locally and I thought it was just a cakewalk, well it was not. I literally used to try every single day to run the script but somehow it would end up giving some errors, I used to google them and apply the results but still, it would fail. But one thing which I consistently did was share my progress regularly with my mentor, Troy no matter if the script used to fail but still I used to communicate that with Troy, I would send him logs and he used to give me some probable solutions for the same but still the script used to fail. I then messaged in the open-horizon-examples group and Joe used to help with my doubts, a huge thanks to him and Troy for helping me figure out things patiently. After over a month on April 1st, the script got successfully executed and then I started to work on the issues assigned by Troy.
These three months taught me to be consistent no matter what the circumstances are and work patiently which I wouldn’t have learned in my college. This experience would no doubt make me a better developer and engineer along with the best practices followed. A timeline of my journey has been shared here.
The LFX Mentorship Program was a great great experience and I did get a great learning curve which I wouldn’t have gotten any other way. The program not only encourages developers to kick-start their open-source journey but also provides some great perks like networking, and learning from the best minds. I would like to thank my mentors Joe Pearson, Troy Fine, and Glen Darling because without their support and patience this wouldn’t have been possible. I would be forever grateful for this opportunity.
Special thanks to my mentor Troy for always being patient with me. These kind words would remain with me always although the program would have ended.
The LF Edge Mentorship program is always a great learning experience, and this year was no exception. Because of Ruchi’s work we now have more services following our best practice policies in the open-horizon-services github repository. Despite the time difference she was always flexible when it came to our sync-ups and was never afraid to ask questions or for clarification if something wasn’t clear. I hope Ruchi will continue to provide the meaningful contributions to the Open Horizon project I have seen her demonstrate throughout this mentorship program.
And yes how can I forget to plug in the awesome swags, special thanks, and gratitude to my mentor Joe Pearson for sending me such cool swags and this super cool note ❤
If you have any queries, connect with me on LinkedIn or Twitter and I would be happy to help you out 😀
As we enter the second half of 2022, we’d like to take a moment to recognize some recent changes to the structure of the LF Edge Governing Board as terms end and new ones begin.
Specifically, we’d like to extend a big THANK YOU to Jason Shepherd of ZEDEDA for his years of service as Chair of the LF Edge Governing Board. During his tenure, LF Edge grew exponentially, adding new projects and new members as well as a library of original resources including white papers, user stories, and more. Additionally, Ryan Anderson of IBM has ended his tenure as the Board’s representative from IBM after years of service. Thank you, Jason and Ryan!
We’d like to welcome our new Board Chair, Tina Tsou, Enterprise Architect, Arm and IBM’s new representative, Hakan Sonmez, Product Strategy & Operations Manager, IBM Edge Computing & Software Defined Networking business unit.
Tina Tsou is an innovator and a visionary with far-reaching accomplishments within the technical engineering realm. As Arm’s Enterprise Architect, Tina serves in the highly visible Technical Lead role for the Enterprise Open Source Enablement team, where she analyzes, designs, and implements robust strategies to establish first tier status for Arm’s architecture within open source communities and projects. Tina also serves as Arm’s Edge Computing Team Lead. As the company’s open source thought leader, she builds powerful partnerships with and influences open source communities in support of multiple architectures.
Tina previously served as the Digital Domain Expert (Connectivity) for Philips Lighting, where she implemented NB-IoT in an outdoor carrier project with China Mobile and Huawei. She released Bluetooth + ZigBee combo chip architecture and delivered a connectivity hardware/software platform (ZigBee 3.0, Wi-Fi). The United States Patent and Trademark Office has granted Tina 100+ patents.
She earned her Bachelor of Computer Science degree from Xi’an University of Architecture and Technologies. Tina was the first woman to chair an Internet Engineering Task Force (IETF) working group from a Chinese business enterprise and was the youngest Asian rapporteur in ITU Telecommunication Standardization Sector (ITU-T) history. She previously served as Chair of the Akraino Technical Steering Committee.
Hakan currently serves as a Product Strategy and Operations Manager at IBM’s Edge Computing and SDN business unit. His focus areas are Edge computing, Hybrid Multi-Cloud Networking and applying AI/ML to multi-cloud operations. He works closely with IBM technical leadership on the Open Horizon project of LF Edge. Previously, he was a lead consultant at IBM Corporate Strategy team.
Prior to IBM, Hakan held various technology roles within the Networking and Telco industry, including Cisco Systems, Airvana Networks, Sycamore Networks and NextWave Wireless.
Hakan holds a BS in Electrical Engineering from Middle East Technical University, an MS in Electrical Engineering from Northwestern University, and an MBA degree from MIT Sloan School of Management.
As a reminder, the Governing Board is made up of members across the project, including one rep from each Premier member; elected General member representatives; and one rep from the Technical Advisory Council. Primary responsibilities include overall management of LF Edge, including budget creation and approvals, oversight of community outreach matters, approval of technical projects, establishment of end-user advisory councils, and more.
Preventive healthcare has always been taught and talked about in most healthcare institutions, but seldom practiced. The collective benefits are immense, and the realization of a fraction of those benefits can help optimize the cost-delivery aspects of healthcare services. It can also act as a catalyst for scaling healthcare services for growing populations in most parts of the world.
The common issues in implementing Preventive Healthcare include providing guidance, educating the vulnerable population masses, and interacting with healthcare professionals. Most of the challenges in regular screenings or monitoring of diabetes, cholesterol, cancer, and mental health are known cost and labor related issues.
Technology has helped to achieve some primary objectives, but the ultimate goal is still far away. IoT and Robotics solutions have been deployed in healthcare facilities and continue to advance quickly. But with new tools, techniques, and cloud infrastructure, it’s time for innovative new solutions that come with emerging IoT devices, like increased connectivity, speed, and lower costs.
Intelligent Edge IoT can provide intense data gathering and processing before sending the prepared data into the Health Ecosystem. Early on, IoT’s were mostly sensors used to gather data and were often cumbersome when it came to software code updates, and they didn’t have enough compute power to run any data processing. The new generation of intelligent IoT’s, which can be deployed at the Edge, can compute and process data ahead of sending it to Cloud computing functions for more sophisticated analysis, including machine learning and deep learning algorithms.
These intelligent IoT’s once deployed as human wearables, facility sensors, etc. can transform EHR (Electronic Health Record) to next generation 24x7x365 EHR (Round the Clock EHR).
This innovation of ‘Round the Clock EHR’ can transform the healthcare industry by bringing healthcare professionals much closer to patients in understanding and diagnosing medical issues early on. It can continuously stream and feed data to ML (machine learning), DL (deep learning) algorithms to help and improve AI solutions. The risk factors can be constantly monitored, thresholds can be adjusted and re-calibrated in real-time with sophisticated software. The Preventive healthcare can not only detect the early signs but also feed it to research facilities for new research programs and eliminate unnecessary analysis.
The Preventive care strategies which also encompass social and environmental conditions can now be monitored with the ‘Round the Clock EHR’, which contains the patient or potential-patient data along with their detailed data on living conditions, geographical conditions, social setup which may trigger onset of a disease or unfavorable conditions. On a larger scale, the Healthcare ecosystem data and ‘Round the Clock EHR’ can provide feed into wider studies to generate health patterns as people move and Preventive Healthcare can lead to new advancements.
As referenced inhttps://www.ncbi.nlm.nih.gov/books/NBK537222/ [Prevention Strategies Lisa A. Kisling; Joe M Das.] – Primary prevention consists of measures aimed at a susceptible population or individual. Secondary prevention emphasizes early disease detection, and its target is healthy-appearing individuals with subclinical forms of the disease. Tertiary prevention targets both the clinical and outcome stages of a disease.
Analyzing these 3 preventive healthcare areas from the ‘Data and Intelligent Edge IoT Lens’, it is apparent that the commonality is gathering, processing, and providing good quality round-the-clock granular data to most sophisticated ML, DL algorithms can definitely transform this area of healthcare transformation.
Our advancing society needs blend of medical and technology research implemented in a way to foster growth, scalability, improvements at lower cost and implementation cycles. These de-coupled solutions are easy to implement and promote wider Healthcare ecosystem in our connected social network.
Each year since its founding, EdgeX Foundry awards at least four members of its contributing community with Contribution and Innovation awards. On this fifth anniversary of the project’s founding, we honored seven distinguished engineers for their efforts on the project.
Innovation Award
Presented to individuals who have provided the most innovative solution to the open-source project over the past year
Byron Nevis and Jim Wang (both from Intel) designed and implemented the new “Delayed Start Services” security capability which is an innovative way to handle providing security tokens to services that start after the EdgeX security framework has started and already handed out tokens to all the known services. Bryon and Jim have led most EdgeX security efforts over the past two years. But this year, they provided some real innovation around securely storing secrets, distributing secrets, and making secrets available to other services. Their innovation will allow adopters to add new device services and application services to EdgeX over time and as use cases demand without requiring a restart of the system.
Anthony Casagrande and Marc Fuller (both from Intel) have been instrumental in Intel’s use of EdgeX to show how it can be a platform in support of retail edge use cases. Anthony/Marc’s work has provided both a device service and an application service to ingest and use RFID information via the LLRP (Low Level Reader Protocol) which is used in bar code reading equipment found in many retail store locations. In addition to their inventions of these ingesting and using services, Anthony and Marc have found (and in some cases fixed) a number of bugs and have identified many needed features (submitting more than 25 Github issues) that real world adopters of the platform need.
Emilo Reyes (again from Intel – there seems to be a theme here!) has been a contributor to the EdgeX DevOps team for the last few years and has been a vital part of making the EdgeX community a better, more streamlined community. Emilio has passion for quality and with his experience around unit testing, has contributed hundreds of tests for our Jenkins global pipeline libraries providing us continuous confidence in our ability to deliver quality code. Emilio also has a passion for automation and has been a driving force behind much of our GitHub automation for EdgeX. For instance, he created automated management of GitHub labels and milestones from a central repository, greatly reducing the number of repetitive tasks needed to manage labels/milestones across 20+ repositories.
Contribution Award
Recognizes individuals who have contributed leadership and helped EdgeX Foundry advance and continue momentum in the past year.
Iain Anderson (from IOTech Systems) – has been a solid contributor, working group leader, and architect/thought leader since the founding of EdgeX in 2017. As Device Service chairman, he has overseen several releases (major and minor) and is currently responsible for 11 active EdgeX device services and helping to usher in 4 more that are in active development. He is the project’s first and foremost authority in C development and personally handles most of the C device service and SDK development. Iain has over 530 commits to the project (almost 50K lines of code) which puts him #6 all time on the project. He is also #7 in all time Pull Request submissions for the project. He is a quiet, steadfast, never-a-complaint contributor that has championed many of the project’s advances such as device service and device profile simplifications, message bus communications from device services, device filtering, and future device service record and replay features.
Siggi Skulason (from Canonical) – updated and significantly improved the EdgeX CLI tool over the course of the last two release cycles. Importantly, Siggi upgraded the tool from the V1 APIs to the V2 APIs, added support for all the service endpoints (versus a small subset selection of APIs), made the CLI available as a snap, removed a significant amount of technical debt, and improved the products help and documentation.
I am happy to honor and call out these gentlemen for their efforts. I don’t know of too many open-source projects that go to honor and thank its contributors with awards like this. To be nominated and then selected by your peers – especially of the caliber of the EdgeX engineering community – is such a great recognition of their work. Congratulations Bryon, Jim, Anthony, Marc, Emilo, Iain and Siggi. Jobs well done.
You can view the Awards presentation here:
Thank You EdgeX
It’s been a productive few months – one release out, another planned and being worked on, and a time to shell out some well earned “kudos”. Before I go, I want to let the community know I am stepping aside from my role as TSC chair, and a new set of leaders are stepping up to take EdgeX into its future – and a bright future it has.
About a month after I joined Dell Technologies in 2015, I was handed the task of finding an IoT software platform to put on our new brand of gateways. EdgeX began life on my kitchen island with an idea, an architecture and a small bit of code. With the support of the company, some great leaders, and a collection of some of the brightest engineers I have ever worked with, my work was expanded on, productized and launched as the edge software you know today. For the past seven years (almost to the day), EdgeX has been at the center of my professional working life (and my wife would probably add that it included much of my personal life). Creating it, taking it into open source, working with the Linux Foundation to make it available and known, working at IOTech to commercialize the idea, and leading this wonderful community has been the highlight of my career. It has allowed me to travel the world, meet so many amazing people, be a part of an incredible creative process, and watch something I started get used to create solutions that help people all over the globe. EdgeX has exceeded even my wildest dreams. It’s just hard to wrap my brain around it even today. I am no Einstein. But if you have an idea (even a moderately good idea), find some amazing people around you that can help turn the idea into reality, and you can catch lightning in a bottle.
I would need a separate blog post to thank everyone I owe for this experience and the success of EdgeX. That is not an exaggeration. “Thank you” is not enough but to the EdgeX community, people I worked with at Dell and those in my current home of IOTech Systems, I hope you take it as a small down payment for all I owe you.
I’ll close by suggesting that if anyone ever offers you the chance to work on, let alone start, an open-source project – jump at the opportunity. You will be better for the experience. I’ll paraphrase Winston Churchill – many forms of software development have been tried and will be tried in this world. No one pretends that open-source development is perfect or all-wise. Indeed, it has been said that open-source development is the worst form of software development except for all other forms that have been tried.
EdgeX – small at the edge, but forever big in my heart.
How did you get involved in the LF Edge community and what is your role now?
