DT-VAR: Decision Tree Predicted Compatibility-Based Vehicular Ad-Hoc Reliable Routing

Abstract

Reliable routing and efficient message delivery in vehicular ad-hoc networks (VANETs) is a significant challenge owing to underlying environment constraints, such as dynamic nature, mobility, and limited connectivity. With the increasing number of machine learning (ML) applications in wireless networks, VANETs can benefit from these data-driven predictions. In this letter, we innovate and investigate ML-based classifications in VANETs to predict the most suitable path with the longest compatibility time and trust using a fog node based VANET architecture. The proposed scheme in SUMO VANET traces achieves up to a 16% packet delivery ratio (PDR) with a 99% accuracy and longer connectivity with only 3 similar to 4 hops, compared with existing AOMDV and TCSR solutions with merely a 4% PDR.