This section contains the upstream information 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 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

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

Expand all Collapse all

Akraino

Upstream - R6 (MEC-based)