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

Road-aware location rectifier
vTrachea-Store

Dependencies of the release (upstream version, patches)

Operating system:

Name	Description
Distributor ID:	Ubuntu
Description:	Ubuntu 20.04.3 LTS
Release:	20.04
Codename:	focal
Note:

Software used:

For	Name	Version/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
	pip3	20.0.2
	lftools:	0.35.10
Running Containers	Docker:	20.10.8
Running Containers	Docker build:	3967b7d
Map and Data-set Generation	SUMO:	1.10.0
	TraceExporter.py	traceExporter (file)
	Netedit:	Netedit - SUMO
	Netconvert:	Netconvert - SUMO
Note: List of software (shown below) are used after release 6
Database	PostgreSQL server:	12.8
Database	DBeaver client:	21.2.2

Repository:

Repository Name	Branch Name	Branch Revision
pred-vanet-mec - gerrit.akraino.org	HEAD	master
Note:

Differences from previous version

Previous versions

Upgrade Procedures

None.

Release Data

Module version changes

None.

Document Version Changes

Initial versions.

Software Deliverable

Road-aware location rectifier (mechanism and internal details can be visualized here)
vTrachea-Store (ERD can be seen on the link)

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

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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