Hack2022 3rd Prize

3^rd Prize Award & Automotive Special Prize

K.I.T.T Knowledge in the traffic

Team

Team from Optare Solutions

• Xose Ramon Sousa Vazquez
• Santiago Rodriguez Garcia
• Fernando Lamela Nieto

Santiago Rodriguez & Fernando Lamela with Jury members Bob Gazda (InterDigital) and Jyoti Sharma (5GAA Board/Verizon)

Introduction

• A connected vehicle (car) that with the support of 5G, MEC and artificial intelligence technologies is able to capture information from the surrounding environment and feed a smart city platform with this information which can predict and adopt several actions for the good of people.

File:Hack2022-optare-architecture1.png

• The information retrieved by the car and sent to the platform is collected by three different ways:
  • camera: the car is equipped with a camera that sends a video stream to the edge over which is executed an AI inference algorithm to detect several patterns and generate information packages relevant to the smart city platform.
  • sensors: the car is equipped with several sensors (temperature, humidity, …) that can create heat maps with this information to the smart city
  • the smart city itself. In regions/areas where the city information sources are isolated (low power, low signal quality, bad coverage due maintenance, weather conditions, etc), the car can act like a link between the information source and the smart city platform, uploading this information in the next available coverage area crossed by the car in its route.

• Every information contribution to the smart city could be rewarded with points that can be exchanged with tax discounts, for example, guaranteeing the participation of the people establishing missions with a different degree of value.

• Every connected car can register on the smart city platform and provide several information about the city and people.

Architecture

Use Cases

UC1: Historical Data collection from autonomous MMTC Edge

• Context: two different networks,
  • Provided by Operator 1, connected, with Edge services in different nodes.
  • Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.

UC.1: a connected car scenario where it can act, once registered, as a carrier of the historical data generated by all the IoT devices connected to the MMTC Edge during an interval of time, in order to be stored and processed by services and the resources provided by the Operator 1 Edge.

• Considerations:
  • MMTC Edge need to be efficient and guaranty IoT Flows based services to local verticals, with limited resources and delivers local 5G connectivity to IoT Devices
  • Edge can have more resources, in addition to 5G connectivity, with backhaul, and can serve applications and services where it can balance between local and cloud.

UC2: Service APP Update to autonomous MMTC Edge

• In the context of two different networks,
  • Provided by Operator 1, connected, with Edge services in different nodes.
  • Provided by Operator 2, autonomous, with MMTC Edge services for different local verticals and with LoraWan connectivity for security monitoring.

UC.2: a connected car scenario where it can act, once registered, as a carrier of new versions, if exist, of MMTC IoT Security Flows, as a part of the ‘firmware’ of the node, that can be updated once security checks were passed.

• Considerations:
  • MMTC Edge provides different functionalities related with the management of IoT devices and data produced, delivering an optimized environment for some verticals in locations with reduced access to broadband communications.
  • MMTC Edge provides secure graphical access for customers to modify their own IoT Flows, but no to update or modify security flows (firmware), that only can be updated by an operator or by a connected car (with this UC) once is registered and security checks are made.

Software resources

• Project repository

https://github.com/flamela/kitt-etsi-hackathon22

Hackathon Alignment

This project has fullfilled the hackathon criteria required by the jury

Innovation		Digital divide of 5g end edge in some areas because its complexity and the cost to bring. The mobile backhaul via vehicular net could help on that proposing a network and computation for agriculture and remote industries
Use-Case / Solution Credibility		Create a platform with capability for sending and receive data using the different edge covered areas and using cheap and autonomous equipment for a quick deployment. Integrated demo is created for hackathon
Use of ETSI MEC Services		MEC013 for location information, MEC012 for radio network information, MEC021 for mobility and depending on the availability of more than one sandbox mep, MEC030 related with V2X and the automotive blueprint MEC011 for application registration, mec service discovery
Use of LF Edge Akraino Blueprints		Checked the Elliot Manager and Nodes. Needs a continuum connectivity among the components and this is not possible in this architecture. Follow some APIs for upload new app images. Tested MEC-based Stable Topology Prediction for Vehicular Networks but similar and more evolved with MEC030
Deliverable		Developed all the sw components running in a demo environment. Designed the autonomous antenna (Spanish self delivery frequency and edge platform). Tested with a wifi connection and a static 5G antenna

Project Videos

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