This section contains the release notes related to release 6 of this blueprint.

Summary

MEC-based Stable Topology Prediction for Vehicular Networks is planned to include the implementation of a platform that can enable stable topology prediction for vehicular networks. It aims at providing vehicle-friendly environment such that the future use-case scenarios can be built upon the provided information.

Components of the release

  1. Road-aware location rectifier
  2. vTrachea-Store

Dependencies of the release (upstream version, patches)

Operating system:

NameDescription
Distributor ID:Ubuntu
Description:Ubuntu 20.04.3 LTS
Release:

 20.04

Codename:focal
Note:


Software used:

ForNameVersion/Info

Running Notebooks

Conda:4.9.2
Python:3.8.10
Jupyter Core:

4.7.1

Jupyter Notebook

6.4.0
Conda Environment File:env_kf_model (file)
Pushing CD Logs

Jenkins:

2.303.1
Docker-hub image link:mehmoodasif/jenkins
pip320.0.2
lftools:0.35.10
Running ContainersDocker:20.10.8
Docker build:3967b7d
Map and Data-set GenerationSUMO:1.10.0
TraceExporter.pytraceExporter (file)
Netedit:Netedit - SUMO
Netconvert:Netconvert - SUMO
Note: List of software (shown below) are used after release 6

Database

PostgreSQL server:12.8
DBeaver client:21.2.2


Repository:

Repository NameBranch NameBranch Revision

pred-vanet-mec - gerrit.akraino.org

HEADmaster
Note:


Differences from previous version

  • Previous versions did not include the the road information
  • Previous version did not have the process of rectification included


Upgrade Procedures

None.


Release Data

Module version changes

None.

Document Version Changes

Initial versions.

Software Deliverable

Documentation Deliverable

Installation Documentation - R6 (MEC-based)

API Documentation - R6 (MEC-based)

Release Notes - R6 (MEC-based) - this document

Fixed Issues and Bugs

None

Enhancements

  • Improvement in the accuracy of estimated/predicted location of a vehicle
  • Support of geo-coordinates rather than using simple x, y coordinates


Functionality changes

  • Previously, the location prediction was done by the use of a basic Kalman filter
  • Now, we have added the step of rectification with the help of road information extracted from OpenStreetMap
  • This procedure enhances the accuracy of estimated/predicted location of a vehicle

New Features

  • Enhancement in accuracy of predicted location, i.e., rectified location

Deliverable

  1. Road-aware rectification (explained here)


Known Limitations, Issues and Workarounds

System Limitations

  • N/A

Known Issues

  • N/A

Workarounds

  • N/A


References

  • N/A


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