# Driving Path Stability in VANETs

## TL;DR

This paper introduces a machine learning-based approach using SVR and LR to predict vehicle movements in VANETs, aiming to enhance routing stability by maintaining only reliable, stable communication paths.

## Contribution

It proposes a novel link prediction method with SVR and LR for VANETs, improving path stability over existing interpolation techniques.

## Key findings

- SVR prediction accuracy is validated against real vehicle traces.
- The proposed method outperforms Lagrange interpolation in stability prediction.
- Error rates demonstrate the effectiveness of machine learning in dynamic vehicular environments.

## Abstract

Vehicular Ad Hoc Network has attracted both research and industrial community due to its benefits in facilitating human life and enhancing the security and comfort. However, various issues have been faced in such networks such as information security, routing reliability, dynamic high mobility of vehicles, that influence the stability of communication. To overcome this issue, it is necessary to increase the routing protocols performances, by keeping only the stable path during the communication. The effective solutions that have been investigated in the literature are based on the link prediction to avoid broken links. In this paper, we propose a new solution based on machine learning concept for link prediction, using LR and Support Vector Regression (SVR) which is a variant of the Support Vector Machine (SVM) algorithm. SVR allows predicting the movements of the vehicles in the network which gives us a decision for the link state at a future time. We study the performance of SVR by comparing the generated prediction values against real movement traces of different vehicles in various mobility scenarios, and to show the effectiveness of the proposed method, we calculate the error rate. Finally, we compare this new SVR method with Lagrange interpolation solution.