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For more Information, please go to: https://wikilf-akraino.akrainoatlassian.orgnet/wiki/x/Qi0wAwJ5fQ
IEC Type3 Android Cloud R6
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For more details, please click the link：https://wikilf-akraino.akrainoatlassian.orgnet/wiki/display/AK/Release+6+Documentation+for+IEC+Type+3%3A+Android+cloud+native+applications+on+Arm+servers+in+edge
Smart Data Transactions for CPS
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- We created a simple framework, implemented on top of the EdgeX IoT microservices stack, demonstrating the sharing of data between edge nodes for local processing and triggering of actuators and alerts, without coordination, for example, requiring coordination by a separate analysis application hosted in the cloud. This framework could remove the requirement for active centralized control applications, or enable services in bandwidth-restricted environments to work collaboratively, sharing only the data that is necessary with only the processes that need it. This framework will be expanded upon in later releases to show how it can be applied to a variety of use cases as different as creating virtual shared entertainment venues and monitoring and control of flood water systems.
- We provide a demonstration implementation of low-power radio connectivity (LoRa) for sensors, as a microservice in the EdgeX device layer. This opens up the possibilities of low-cost, low-power, and low-maintenance networks of sensors in large environments, e.g. chemical plants or agricultural settings, with many sensors and a few or even a single gateway device and almost no cabling.
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Oleg Berzin Jim Xu (Deactivated)
Edge is becoming new norm enabling innovations across multiple industries. Akraino is a set of open infrastructures and application blueprints for the Edge, spanning a broad variety of use cases, including 5G, AI, Edge IaaS/PaaS, IoT, for both provider and enterprise edge domains. These Blueprints have been created by the Akraino community and focus exclusively on the edge in all of its different forms. What unites all of these blueprints is that they have been tested by the community and are ready for adoption as-is, or used as a starting point for customizing a new edge blueprint.
More information, please refer to https://www.lfedge.org/projects/akraino/.Ike Alisson
Android Cloud
Overview
Learn how Robox is deployed in the ysemi test lab. In this new example, the deployment of robox to the ysemi test lab is shown, and the cloud gaming platform can
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armv9 architecture server chip, the cost of the entire solution will be greatly reduced. More information, please refer to:
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Companies
Fujitsu and Ritsumeikan are contributing to framework for fusion of robots and sensors.
The framework combines sensor data collection, machine and deep learning models for analysis, and feedback for mechanical robot control.
Signalogic is contributing to the SESS blueprint automated speech recognition (ASR) functionality.
A 20,000 word real-time vocabulary is being implemented on a pico ITX Atom board (quad-core, 3.5" x 3.5", 10 W) suitable for
Ritsumeikan's food prep and production use cases, as well as a range of robotics use cases in manufacturing, production, agriculture, and retail.
The implementation includes robust audio noise processing to deal with background and robot mechanical noise.
Challenges
Robotics is an important tool for achieving the SDGs. Workers will be able to focus on decent work and new innovation by improvement of labor productivity using robot, as a result, they can move toward new economic growth. However, there are industries where it is difficult to apply current robotics. For example, agriculture, restaurant, food factory, etc.. The challenges current robotics faces in these industries are
- Objects with diverse shapes, flexibility, and frictional properties
- Uncertain environment
- High-mix small-lot production
Solutions
Ritumeikan and Fujitsu research and develop enhancement of cognitive ability to solve these challenges. The features are the following three points.
- Sensor-rich technology for multi-dimensional data acquisition
- AI/IoT technology with force/contact information
- IoT maintenance and inspection technology
This blueprint family provides open software stack which can implement above cognitive ability easy.
Results
The BP will allow robotic system integrators to easily implement systems that combine sensors and robots. We plan to enhance the following functions in 2022.
- Analysis of sensor data and feedback to robot
- Lightweight speech recognition to recognize "immediate and urgent" voice commands while protecting data privacy
LF Edge Cross Project Collaboration
Companies
Challenges
Solutions
Results
DevOps MEC Infra Orchestration
Companies
Challenges
Solutions
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Show Case details:
Overview:
Demo of LFEdge Cross projects(Akraino EALTEdge + EdgeGallery + eKuiper + Fledge) collaboration to deliver Edge computing platform with IOT stack.
Background:
EALTEdge (Enterprise applications on lightweight 5G telco edge) BP from Akraino, integrate various open source projects to build a MEC based edge computing platform.
EALTEdge BP along with its upstream project EdgeGallery, provide an IOT stack which leverages Fledge(for IOT protocol and data collection) and eKuiper(Data Filter).
For using the EALTEdge Edge computing platform and experience the profile based IOT solution, EALTEdge is deployed in LFEdge Lab.
User with right permission to lab, can connect and try it.
Details about use case:
In this demo, we use a sample simulated IOT device.
Data from Device is processed in pipeline in multiple stages like data collection from devices, data filtration and transformation then store in DB for offline scenarios.
Now IOT applications can access this data. It support http exporter to get data by application.
Application like grafana can get data from DB as well.
In this Demo, we are using simulated MQTT device which produce readings every seconds and processed data is visualise in Grafana to monitor the device.