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For more details, please click the link：https://wiki.akraino.org/display/AK/Release+6+Documentation+for+IEC+Type+3%3A+Android+cloud+native+applications+on+Arm+servers+in+edge
Smart Data Transactions for CPS
The Smart Data Transactions for CPS (Cyber-Physical Systems) blueprint in Release 6 demonstrates two technologies for expanding the range of available edge solutions:
- 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, 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.
The blueprint is designed as a simple to deploy and pre-tested implementation which can be built upon to create new edge services that make use of these technologies.
More information is available at the blueprint's wiki page and the Release 6 blueprint documentation.
Case Studies
// Example: https://www.lfedge.org/resources/case-studies/
Overview
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/.
Android Cloud
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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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Robotics
Companies
Ysemi Computing was founded in 2020. The company's core team consists of core R&D and operation executives from first-class domestic and foreign companies such as Arm, Intel, Huawei,
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and features of each core and peripherals of CPU server chips. The cost per container is more lower, the power consumption per containeris more lower, and each arm server could run more containers.
Robotics
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
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.
DevOps MEC Infra Orchestration
Public Cloud Edge Interface (PCEI) enables infrastructure orchestration and cloud native application deployment across public clouds (core and edge), edge clouds, interconnection providers and network operators. The notable innovations in PCEI are the integration of Terraform as a microservice to enable DevOps driven Infrastructure-as-Code provisioning of edge cloud resources (bare metal servers, operating systems, networking) public cloud IaaS/SaaS resources, private and public interconnection between edge cloud and public cloud, integration of Ansible as a microservice to enable automation of configuration of infrastructure resources (e.g., servers) and deployment of Kubernetes and its critical components (e.g., CNIs) on the edge cloud, and introduction of a workflow engine to manage the stages and parameter exchange for infrastructure orchestration and application deployment as part of a composable workflow. PCEI helps simplify the process of multi-domain infrastructure orchestration by enabling a uniform representation of diverse services, features, attributes, and APIs used in individual domains as resources and data in the code that can be written by developers and executed by the orchestrator, effectively making the infrastructure orchestration across multiple domains DevOps-driven.
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