There are different use cases for uMEC, and in fact we have used it as a basis for competitions to invent and implement them.
The challenge was to create example demos of new digital services and business opportunities in a real smart city using LuxTurrim5G and uMEC. The challenge description also had a sample code to help getting started.
Please click here to read more and find photos about the event.
The winners implemented Moose ETWS (Early Traffic Warning System).
This system consists of a phone application that a user can use for registering to the service and for receiving warnings of roaming moose in the area.
The uMEC nodes have cameras and run a Tensorflow algorithm to detect the moose. When the user is driving in that area a warning will be sent. With this application, detection can be done on the uMEC node and there is no need to send the whole live image to a data center for analysis.
The runner up team implemented Fluffy Hounder. It is a service for pet owners: the uMEC nodes recognize and identify dogs, cats and other animals. If a pet gets lost, it can be detected automatically. The pet owner will pay a monthly fee for the service.
The challengers of the Open Open City track were asked to demonstrate use cases and create a prototype of an open data platform which can collect, analyze and share weather, traffic, emergency information with citizens.
Detailed challenge description and APIs were provided for all participants.
The winner team was Flowify, who used openSUSE Tumbleweed base containers for their backend which ran entirely on a uMEC Edge gateway node. They used state-of-the-art image detection to analyze static images taken by the security cameras. A people count is extracted from the pictures and collected. Pictures were taken via a uMEC API. The data can be utilized in city planning and design of shopping malls and other places.
The one man team became 1st runner of. He created a dashboard with an SMS backhaul for managing uMEC clusters.
Source code available.
The 2nd runner up was Ravenholm. They brought a business case, where uMEC deployments would help city officials, construction site planners and project managers to better manage the effects of large, open air building projects.
Source code available.
During fall 2019, students from the Metropolia University used the platform and created a new service according to the ETSI MEC framework, using OpenAPI to generate APIs and skeleton code.
Separate teams were responsible for different areas of the project.
They have built a backend infrastructure that consists of 4 servers and a couple of Raspberry Pi 3B+ uMEC nodes.
The teams created a CI / CD environment with Jenkins, a comprehensive performance monitoring and visualisation with Grafana and improved the Moose ETWS application for container based installations.