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Home Edge

Embedded IoT 2021 – Intro of the research insight from the presentation based on LF Edge HomeEdge

By Blog, Home Edge

The blog post initially appeared on the Samsung Research blog

By Suresh LC and Sunchit Sharma, of SRI-Bangalore

Introduction

Every home is flooded with consumer electronic devices which have capability to connect to internet. These devices can be accessed from anywhere in the world and thus making the home smart. As per a survey the size of smart home market would be $6 Billion by 2022. As per Gartner report 80% of all the IoT projects to include AI as a major component.

In conventional cloud based setup following two things need to be considered with utmost importance:

Latency – As per study there is latency of 0.82ms for every 100 miles travelled by data

Data Privacy – Average total cost of data breach is $3.92 Million

With devices of heterogeneous hardware capabilities the processing which were traditionally performed at cloud are being moved to devices if possible as shown below.

These lead us to work towards developing an edge platform for smart home system. As an initial step the Edge Orchestration project was developed under the umbrella of LF Edge.

Before getting into the details of Edge orchestration, let us understand few terms commonly used in this.

Edge computing – a distributed computing paradigm that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth

Devices : Our phones, tablets, smart TVs and so that are now a part of our homes

Inferencing – act or process of reaching a conclusion from known facts.

ML perspective – act of taking decisions / giving predictions / aiding the user based on a pre trained model.

Home Edge – Target

Home Edge project defines uses cases, technical requirements for smart home platform. The project aims to develop and maintain features and APIs catering to the above needs in a manner of open source collaboration. The minimum viable features of the platform are below:

Dynamic device/service discovery at “Home Edge”

Service Offloading

Quality of Service guarantee in various dynamic conditions

Distributed Machine Learning

Multi-vendor Interoperability

User privacy

Edge Orchestration targets to achieve easy connection and efficient resource utilization among Edge devices. Currently REST based device discovery and service offloading is supported. Score is calculated based on resource information (CPU/Memory/Network/Context) by all the edge devices. The score is used for selecting the device for service offloading when more than one device offer same service. Home Edge also supports Multi Edge Communication for multi NAT device discovery.

Problem Statement

As mentioned above the service offloading is based on score of all the devices. Score is in-turn is calculated using CPU/Memory/Network/Context. These values are populated in the db initially when home edge is installed in the device. And hence these are more static values.

Static Score is a function of

Number of CPU cores in a machine

Bandwidth of each CPU core

Network bandwidth

Round trip time in communication between devices

For example say at time t1, device B has available memory ‘X’ and hence this value is stored in db. Say at time t2 device A requests device B for score and at t2 the available memory in device B is ‘X-Y’. But the score is calculated using ‘X’ which has been stored initially.

Say in another scenario device A requests device B for service offloading. Device B starts executing the service. But say in the mid of execution moves away from the network. In such cases it would be unsuccessful offloading.

We could see in the above situations that the main purpose of Home Edge is gone for a task.
And these lead to the proposal of dynamic score calculation which is more effective and efficient.
Properties of a home based Distributed Inferencing System

We propose an effective method for score calculation. The proposed method for score calculation is based on following points:

1. Model Specific Device Selection

2. Efficient Data Distribution

3. Efficient Churn Prediction

4. Non Rigid and Self Improving

Based on the above four properties we defined a feedback based system that can help us achieve optimal data distribution for parallel inferencing in a heterogeneous system.

High Level System

Following diagram shows the high level architecture of the system. Let us take a use case and see the flow of tasks. For example when a user queries to Speaker “Let me know is any visitors today?”. The User query is indigested by the query manager to understand the query. The user query is to identify the number of visitors who visited today. The Data Source needs to be identified for query execution say the Doorbell Camera/Backyard CCTV. Then the model required for this purpose is identified and the devices which have the service (model). Based on the past performance, static score, trust of the device and number times previously the model has ran on the device, the devices are selected. Priority of the devices are calculated and based on the same the data is split among the devices. The result from the devices are aggregated to calculate the final output.

Understanding Score

Total Score is a function of Static score and Memory usage of the device

Changes in Score can help us predict estimated runtimes at the current time

Understanding Time Estimates

Time Estimates are calculated based on previous time records, average and current score

Data Points which are stored :

1. Average Score

2. Average time taken per inference

3. Number of times a model has been deployed on a device

4. Success probability of a model running on a device – Trust

Estimating runtime on Devices

Runtime estimation is done post sufficient data points being collected in the system

Delta Factor – A liner relationship factor to approximate the change in runtimes when score changes is calculated

Estimated time – using the Delta factor, Average Score and Average Runtime and the Current score of the device

Understanding Churn Probability Estimation

Logical buckets for every 5 minute frames is created to know the availability of devices

In case a bucket is polling a device for nth time and the was available k times before the new probability would be

1. (K+1)/(n+1), if device is available

2. K/(n+1), if device unavailable

Calculating Priority

Post estimation of runtimes, The probability of devices moving out of the system are checked and devices whose probability falls low are not kept in the consideration list

All devices in the consideration list are sorted based on the product of the trust and inverse of runtimes

Top K devices out of N are selected and data is distributed among them

Data Distribution

Data on the devices is distributed in the inverse ratio of the estimated runtimes to minimize the total time the system takes to make the complete inference

Feedback

Post execution the actual runtimes and scores of the devices are used to adjust the average in the databases

In case of failure next device is selected and the trust value of the device is adjusted in database

Test Run on Simulator

Aim – To demonstrate heuristic based data distribution among devices
To Test – Note the devices that work best for a model, checking the selection irrespective of device scores

Web based simulator was developed to demonstrate the proposed method. Following is a sample screenshot of the simulator

 

In the due course of our work we also found few models execute better in specific devices. To check up on this we had run two use case – Camera Anomaly detection and Person identification. And we found that the models used in both the scenarios gave better execution results in different devices as shown below.

Home Edge Earns the CII Best Practices Badge

By Blog, Home Edge

Home Edge

Written by Taras Drozdovsky and Taewan Kim, LF Edge Home Edge Committer & Staff Engineer at Samsung Research as well as Peter Moonki Hong, LF Edge Governing Board member & Principal Engineer at Samsung Research

Every year the number of Free/Libre and Open Source Software (FLOSS) projects increases and the community has a goal to maintain high quality code, documentation, test coverage and, of course, a high level of security.

The Linux Foundation, in collaboration with and major IT companies in its Core Infrastructure Initiative (CII), has collected best practices for FLOSS projects and provided these criteria as CII Best Practices.

In December 2020, LF Edge’s Home Edge team set itself the goal of getting a CII Best Practices Badge. After working tirelessly on the requirements, we are happy to announce Home Edge has received the CII Best Practices Badge!

Thank you to the committers and key contributors of Home Edge, Taras Drozdovskyi, Taewan Kim, Somang Park, MyeongGi Jeong, Suresh L C, Sunchit Sharma, Dwarkaprasad Dayama, Peter Moonki Hong. We would also like to thank Jim White with EdgeX Foundry for helping to guide us and the entire LF Edge/Linux Foundation team for their support.

Benefits achieved on the way to getting the CII Best Practices passing badge:

  • Improved documentation:
    • Security and Testing policy
    • How to Contributing Guide
    • Descriptions External APIs
  • Improved the build and testing system
    • CI infrastructures: Github->Actions – 20 checks
    • Integrated of external software tools for analysis code:
      • gofmt – 92%;
      • go_vet – 100%;
      • golint – 76%;
      • SonarCloud: Security Hotspots – 37 -> 0; Code Smells – 253 -> 50; Duplications – 7.8% -> 3%
    • Improved security analysis:
      • Integrated CodeQL Analysis, LGTM services: 17 -> 0 Security Alerts

We have reached a high level of code quality, but continue to improve our code!

Improving the Edge Orchestration project infrastructure (using many tools for analyzing code and searching for vulnerabilities) allowed us to increase not only the level of security, but also the reliability of our product. We hope that improving the documentation will reduce the time to enter the project, and therefore increase the number of external developers participating in the project. Their advice and input are very important for us.

It should also be noted that there were many areas of self-development for the members of the project team: developers became testers, technical writers, security officers. This is a wonderful experience.

Next step of Edge Orchestration team

Further improving the Edge-Home-Orchestration project and achieving “silver” and “gold” badges. Implementation of OpenSSF protected code development practices intoEdge-Home-Orchestration.

If you would like to learn more about the use cases for Home Edge or more technical details, check out the video of our October 2020 webinar. As part of the “On the Edge with LF Edge” webinar series, we shared the general overview for the project, how it fits into LF Edge, key features of the Coconut release, the roadmap, how to get involved and the landscape of the IoT Home Edge.

If you would like to contribute to Home Edge or share feedback, find the project on GitHub, the LF Edge Slack channel (#homeedge) or subscribe to our email list (homeedge-tsc@lists.lfedge.org). We welcome new contributors to help make this project better and expand the LF Edge community.

Additional Home Edge Resources:

1. Coconut release code : https://github.com/lf-edge/edge-home-orchestration-go/releases/tag/coconut

2. Release notes

3. Home Edge Wiki: https://wiki.lfedge.org/display/HOME/Home+Edge+Project

 

Home Edge Launches Coconut Release

By Announcement, Blog, Home Edge

Home Edge

Written by Moonki Hong, LF Edge Governing Board Member, lead for Home Edge and Staff Engineer at the Samsung Research Open Source Group

Home Edge is a robust, reliable and intelligent home edge computing open source framework and ecosystem running on a variety of devices at home. To accelerate the deployment of the edge computing services ecosystem successfully, LF Edge’s 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.

Home Edge is made up of multiple modules to allow for a flexible deployment.  The Edge Orchestration Module handles Edge (device) Discovery, Service Offloading (load balancing between devices); Edge Setup, and Service Management and Monitoring.  The Data Storage Module provides persistent storage (Core Data) and Metadata to identify the node.  The DS Module also consists of the I/O Agent that, via APIs, allows for the accessing of the data.  The Home Device Control Module provides device discovery and setup.  The Home Device Client allows for the connection between the Cloud Interface and the Home Device Client (controller for the home devices.  There are also modules for Machine Learning, Security, and a Deep Neural Network Framework.

Today, Home Edge is happy to announce the launch its Coconut release. The third release for the project, Coconut includes new features such as Multi-NAT communications (which enables discovery of devices in different NATs) and Data Storage.  

In collaboration with EdgeX Foundry, a centralized device can be designated as a primary device to store the data from different devices. The Home Edge project appreciates those who have consistently supported and helped us with this release.

We would especially like to thank to the EdgeX Foundry team, specifically TSC Chair Jim White and Cloud Tsai from IoTech, and Taewan Kim, Ayush, Sunchit, Nitu and others at Samsung that helped develop and debug the core features of the Coconut release. In addition, I would like to express my gratitude to Suresh LC, who has contributed all his passions to his advocate role to promote Home Edge’s technical and business perspectives in the LF Edge TAC and other committees.

The Coconut Release builds on the features of the Baobab release, which was launched last year. Home Edge expects its next release will be available in 2021 and will include real time data analytics features. 

If you would like to learn more about the use cases for Home Edge or more technical details, check out the video of our October 2020 webinar. As part of the “On the Edge with LF Edge” webinar series, we shared the general overview for the project, how it fits into LF Edge, key features of the Coconut release, the roadmap, how to get involved and the landscape of the IoT Home Edge.

If you would like to contribute to Home Edge or share feedback, find the project on GitHub, the LF Edge Slack channel (#homeedge) or subscribe to our email list (homeedge-tsc@lists.lfedge.org). We welcome new contributors to help make this project better and expand the LF Edge community.

Additional Home Edge Resources:

1. Coconut release code : https://github.com/lf-edge/edge-home-orchestration-go/releases/tag/coconut

2. Release notes

3. Home Edge Wiki: https://wiki.lfedge.org/display/HOME/Home+Edge+Project

 

On the “Edge” of Something Great

By Akraino, Announcement, Baetyl, Blog, EdgeX Foundry, Fledge, Home Edge, LF Edge, Open Horizon, Project EVE, Secure Device Onboard, State of the Edge

As we kick off Open Networking and Edge Summit today, we are celebrating the edge by sharing the results of our first-ever LF Edge Member Survey and insight into what our focuses are next year.

LF Edge, which will celebrate its 2nd birthday in January 2021, sent the survey to our more than 75 member companies and liaisons. The survey featured about 15 questions that collected details about open source and edge computing, how members of the LF Edge community are using edge computing and what project resources are most valuable. 

Why did you chose to participate in LF Edge?

The Results Are In

The Top 3 reasons to participate in LF Edge are market creation and adoption acceleration, collaboration with peers and industry influence. 

  • More than 71% joined LF Edge for market creation and adoption acceleration
  • More than 57% indicated they joined LF Edge for business development
  • More than 62% have either deployed products or services based on LF Edge Projects or they are planned by for later this year, next year or within the next 3-5 years

Have you deployed products or services based on LF Edge Projects?

This feedback corresponds with what we’re seeing in some of the LF Edge projects. For example, our Stage 3 Projects Akraino and EdgeX Foundry are already being deployed. Earlier this summer, Akraino launched its Release 3 (R3) that delivers a fully functional open source edge stack that enables a diversity of edge platforms across the globe. With R3, Akraino brings deployments and PoCs from a swath of global organizations including Aarna Networks, China Mobile, Equinix, Futurewei, Huawei, Intel, Juniper, Nokia, NVIDIA, Tencent, WeBank, WiPro, and more. 

Additionally, EdgeX Foundry has hit more than 7 million container downloads last month and a global ecosystem of complementary products and services that continues to increase. As a result, EdgeX Foundry is seeing more end-user case studies from big companies like Accenture, ThunderSoft and Jiangxing Intelligence

Have you gained insight into end user requirements through open collaboration?


Collaboration with peers

The edge today is a solution-specific story. Equipment and architectures are purpose-built for specific use cases, such as 5G and network function virtualization, next-generation CDNs and cloud, and streaming games. Which is why collaboration is key and more than 70% of respondents said they joined LF Edge to collaborate with peers. Here are a few activities at ONES that showcase the cross-project and members collaboration. 

Additionally, LF Edge created a LF Edge Vertical Solutions Group that is working to enable easily-customized deployments based on market/vertical requirements. In fact, we are hosting an LF Edge End User Community Event on October 1 that provides a platform for discussing the utilization of LF Edge Projects in real-world applications. The goal of these sessions is to educate the LF Edge community (both new and existing) to make sure we appropriately tailor the output of our project collaborations to meet end user needs. Learn more.

Industry Influence

More than 85% of members indicated they have gained insights into end user requirements through open collaboration. A common definition of the edge is gaining momentum. Community efforts such as LF Edge and State of the Edge’s assets, the Open Glossary of Edge Computing, and the Edge Computing Landscape are providing cohesion and unifying the industry. In fact,  LF Edge members in all nine of the projects collaborated to create an industry roadmap that is being supported by global tech giants and start-ups alike.

 

 

Where do we go from here? 

When asked, LF Edge members didn’t hold back. They want more. They want to see more of everything – cross-project collaboration, end user events and communication, use cases, open source collaboration with other liaisons. As we head into 2021, LF Edge will continue to lay the groundwork for markets like cloud native, 5G, and edge for  more open deployments and collaboration.  

 

LF Edge Expands Ecosystem with Open Horizon, adds Seven New Members and Reaches Critical Deployment Milestones

By Akraino Edge Stack, Announcement, Baetyl, EdgeX Foundry, Fledge, Home Edge, LF Edge, Open Horizon, Project EVE, State of the Edge

  • Open Horizon, an application and metadata delivery platform, is now part of LF Edge as a Stage 1 (At-Large) Project.
  • New members bring R&D expertise in Telco, Enterprise and Cloud Edge Infrastructure.
  • EdgeX Foundry hits 4.3 million downloads and Akraino R2 delivers 14 validated deployment-ready blueprints.
  • Fledge shares a race car use case optimizing car and driver operations using Google Cloud, Machine Learning and state-of-the-art digital twins and simulators.

SAN FRANCISCO – April 30, 2020 –  LF Edge, an umbrella organization under The Linux Foundation that aims to establish an open, interoperable framework for edge computing independent of hardware, silicon, cloud, or operating system, today announced continued project momentum with the addition a new project and several technical milestones for EdgeX Foundry, Akraino Edge Stack and Fledge. Additionally, the project welcomes seven new members including CloudBrink, Federated Wireless, Industrial Technology Research Institute (ITRI), Kaloom, Ori Industries, Tensor Networks and VoerEir to its ecosystem.

Open Horizon, an existing project contributed by IBM, is a platform for managing the service software lifecycle of containerized workloads and related machine learning assets. It enables autonomous management of applications deployed to distributed webscale fleets of edge computing nodes and devices without requiring on-premise administrators.

Edge computing brings computation and data storage closer to where data is created by people, places, and things. Open Horizon simplifies the job of getting the right applications and machine learning onto the right compute devices, and keeps those applications running and updated. It also enables the autonomous management of more than 10,000 edge devices simultaneously – that’s 20 times as many endpoints as in traditional solutions.

“We are thrilled to welcome Open Horizon and new members to the LF Edge ecosystem,” said Arpit Joshipura, general manager, Networking, Edge & IoT, the Linux Foundation. “These additions complement our deployment ready LF Edge open source projects and our growing global ecosystem.”

“LF Edge is bringing together some of the most significant open source efforts in the industry, said Todd Moore, IBM VP Open Technology, “We are excited to contribute the Open Horizon project as this will expand the work with the other projects and companies to create shared approaches, open standards, and common interfaces and APIs.”

Open Horizon joins LF Edge’s other projects including: Akraino Edge Stack, Baetyl,  EdgeX Foundry, Fledge, Home Edge, Project EVE and State of the Edge. These projects support emerging edge applications across areas such as non-traditional video and connected things that require lower latency, and  faster processing and mobility. By forming a software stack that brings the best of cloud, enterprise and telecom, LF Edge helps to unify a fragmented edge market around a common, open vision for the future of the industry.

Since its launch last year, LF Edge projects have met significant milestones including:

  • EdgeX Foundry has hit 4.3 million docker downloads.
  • Akraino Edge Stack (Release 2) has 14 specific Blueprints that have all tested and validated on hardware labs and can be deployed immediately in various industries including Connected Vehicle, AR/VR, Integrated Cloud Native NFV, Network Cloud and Tungsten Fabric and SDN-Enabled Broadband Access.
  • Fledge shares a race car use case optimizing car and driver operations using Google Cloud, Machine Learning and state-of-the-art digital twins and simulators.
  • State of the Edge merged under LF Edge earlier this month and will continue to pave the path as the industry’s first open research program on edge computing. Under the umbrella, State of the Edge will continue its assets including State of the Edge Reports, Open Glossary of Edge Computing and the Edge Computing Landscape.

Support from the Expanding LF Edge Ecosystem

Federated Wireless:

“LF Edge has become a critical point of collaboration for network and enterprise edge innovators in this new cloud-driven IT landscape,” said Kurt Schaubach, CTO, Federated Wireless. “We joined the LF Edge to apply our connectivity and spectrum expertise to helping define the State of the Edge, and are energized by the opportunity to contribute to the establishment of next generation edge compute for the myriad of low latency applications that will soon be part of private 5G networks.”

Industrial Technology Research Institute (ITRI):

“ITRI is one of the world’s leading technology R&D institutions aiming to innovate a better future for society. Founded in 1973, ITRI has played a vital role in transforming Taiwan’s industries from labor-intensive into innovation-driven. We focus on the fields of Smart Living, Quality Health, and Sustainable Environment. Over the years, we also added a focus on 5G, AI, and Edge Computing related research and development. We joined LF Edge to leverage its leadership in these areas and to collaborate with the more than 75 member companies on projects like Akraino Edge Stack.”

Kaloom:

“Kaloom is pleased to join LF Edge to collaborate with the community on developing open, cloud-native networking, management and orchestration for edge deployments” said Suresh Krishnan, chief technology officer, Kaloom.  “We are working on an unified edge solution in order to optimize the use of resources while meeting the exacting performance, space and energy efficiency needs that are posed by edge deployments. We look forward to contributing our expertise in this space and to collaborating with the other members in LF Edge in accelerating the adoption of open source software, hardware and standards that speed up innovation and reduce TCO.”

Ori Industries:

“At Ori, we are fundamentally changing how software interacts with the distributed hardware on mobile operator networks.” said Mahdi Yahya, Founder and CEO, Ori Industries. “We also know that developers can’t provision, deploy and run applications seamlessly on telco infrastructure. We’re looking forward to working closely with the LF Edge community and the wider open-source ecosystem this year, as we turn our attention to developers and opening up access to the distributed, telco edge.”

Tensor Networks:

“Tensor Networks believes in and supports open source. Having an arena free from the risks of IP Infringement to collaborate and develop value which can be accessible to more people and organizations is essential to our efforts. Tensor runs its organization, and develops products on top of Linux.  The visions of LF Edge, where networks and latency are part of open software based service composition and delivery, align with our vision of open, fast, smart, secure, connected, and customer driven opportunities across all industry boundaries.” – Bill Walker, Chief Technology Officer.

VoerEir:

“In our extensive work with industry leaders for NFVI/VIM test and benchmarking,  a need to standardize infrastructure KPIs in Edge computing has gradually become more important,” said Arif  Khan, Co-Founder of VoerEir AB. “This need has made it essential for us to join LF Edge and to initiate the new Feature Project “Kontour” under the Akraino umbrella. We are excited to collaborate with various industry leaders to define, standardize  and measure Edge KPIs.”

About The Linux Foundation

Founded in 2000, the Linux Foundation is supported by more than 1,000 members and is the world’s leading home for collaboration on open source software, open standards, open data, and open hardware. Linux Foundation’s projects are critical to the world’s infrastructure including Linux, Kubernetes, Node.js, and more.  The Linux Foundation’s methodology focuses on leveraging best practices and addressing the needs of contributors, users and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page: https://www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

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LF Edge in 2020: Looking back and Revving forward

By Akraino Edge Stack, Baetyl, Blog, EdgeX Foundry, Fledge, Home Edge, Open Glossary of Edge Computing, Project EVE

Written by Melissa Evers-Hood, LF Edge Governing Board Chair 

Dear Community,

Happy New Year! As we kick off 2020, I wanted to send a note of thanks and recognition to each of you for a wonderful 2019, which marked several meaningful accomplishments for this organization.  LF Edge was launched in Jan 2019 with an aim to unify the edge communities across IOT, Telco, Enterprise and Cloud providing aligned open source edge frameworks for Infrastructure and Applications.

Our accomplishments include:

  • EdgeX Foundry has blossomed this year in participation, downloads, and use cases. EdgeX, as folks commonly call it, also graduated to Impact project stage and surpassed 1.5 million container downloads in 2019.
  • Akraino, which also reached Impact stage this year, is preparing for it’s second release with 5 new blueprints for R2, with updates to 9 of the existing 10 R1 blueprints already released. Most notably, its broadening its blueprint profile to include new blueprints for Connected Vehicles and AR/VR, truly becoming a viable framework across edge applications.
  • At the Growth Stage, Open Glossary provides common terminology and ecosystem mapping for the complex Edge environment. In 2019, the Glossary Project shipped 2.0 of the Glossary, which was integrated into the 2020 State of the Edge Report. The Glossary Project began the process of helping to standardize terminology across all LF Edge projects, and also launched the LF Edge Landscape Project: https://landscape.lfedge.org/.
  • Also at the Growth Stage, Project Eve allows cloud-native development practices in IOT and edge applications. EVE’s most recent release, 4.5.1 (which was gifted on December 25, 2019), provides a brand new initramfs based installer, ACRN tech preview, and ARM/HiKey support.
  • The Home Edge project, targeted to enable a home edge computing framework, announced their Baobab release in November. The Home Edge Project has initiated cross-project collaboration with EdgeX Foundry (secure data storage) and Project EVE (containerized OS).
  • We also added 2 additional projects this year.
    • Baetyl which provides an open source edge computing platform.
    • Fledge which is an open source framework and community for the industrial edge focused on critical operations, predictive maintenance, situational awareness and safety. Fledge has recently begun cross-project collaboration with Project EVE and Akraino, with more information available here.
  • Our reach has broadened with 9k articles, almost 50k new users, and 6.7M social media impressions.

I am excited about the work ahead in 2020, especially as we celebrate our one year anniversary this month. We laid the foundation last year – offered a solution to unite the various edge communities – and now, with your support and contributions, we’re ready to move to the next phase.

LF Edge is co-hosting Open Networking & Edge Summit in April and our teams are working hard on several cross-project demos and solutions. We’re planning meetups and other F2F opportunities at the show, so this conference will be a must.

Our focus as a community will be to continue to expand our developers and end users.  We will do this through having agile communities, that collaborate openly, create secure, updateable, production ready code, and work together as one. We also expect that there will be new projects to join and integrate.  As we walk into this bright future, working as a unified body will demonstrate that the fastest path to Edge products is through LF Edge.

I look forward to working with each of you in ‘20 and seeing you in Los Angeles this April at ONES!

Melissa

Edge Computing at IoT Solutions World Congress 2019

By Blog, EdgeX Foundry, Home Edge, Project EVE

Every year one of the world’s largest Internet of Things trade shows, IoT Solutions World Congress, is held in Barcelona, Spain. It brings together device manufacturers, service providers, AI & ML companies and solutions integrators from around the world to share information about their products and the state of IoT ecosystems. Filling multiple convention halls at the Fira Barcelona center, and featuring the biggest names in IoT and technology, you can spend days walking the expo hall and talking to vendors.

Crowd at the LF Edge Booth

This wasn’t the first time the EdgeX Foundry has had a booth at IOTSWC, but this year they were joined by other LF Edge projects, specifically Home Edge and Project EVE, to present solutions across the edge landscape. Our booth was staffed by project contributors from all over the world, from the US and Europe to India and Taiwan, and featured real world examples of the open source technology that is being developed under the LF Edge umbrella.  Not only did our members get a chance to learn about each other’s projects during this time, they were able to explain those other projects to the visitors to our booth. It was truly a community coming together to support and promote the LF Edge as a whole.

EdgeX Smart Building Demo EVE deployments on a wind turbine

We spoke with thousands of people over the 3 days of conference, and gave countless demonstrations. One notable change in conversations from a year ago is that most attendees we spoke to this year already knew and understood the importance of edge computing, and were looking for specific solutions to the problems that they are now facing. And while many vendors at the show offered some of these solutions, only the LF Edge projects offered open, vendor agnostic platforms that prevent lock-in and promote an ecosystem of 3rd party development around commonly developed core.

Selfie of the LF Edge booth staffIf you missed us at IOTSWC, you can join our projects online where we have a public Slack, mailing lists and host our meetings in the open. You can also look for us at events in 2020!