Flexible, Portable, and Practicable Solution for Routing in VANETs: A Fuzzy Constraint Q-Learning Approach

Abstract

Vehicular ad hoc networks (VANETs) have been attracting interest for their potential uses in driving assistance, traffic monitoring, and entertainment systems. However, due to vehicle movement, limited wireless resources, and the lossy characteristics of a wireless channel, providing a reliable multihop communication in VANETs is particularly challenging. In this paper, we propose PFQ-AODV, which is a portable VANET routing protocol that learns the optimal route by employing a fuzzy constraint Q-learning algorithm based on ad hoc on-demand distance vector (AODV) routing. The protocol uses fuzzy logic to evaluate whether a wireless link is good or not by considering multiple metrics, which are, specifically, the available bandwidth, link quality, and relative vehicle movement. Based on an evaluation of each wireless link, the proposed protocol learns the best route using the route request (RREQ) messages and hello messages. The protocol can infer vehicle movement based on neighbor information when position information is unavailable. PFQ-AODV is also independent of lower layers. Therefore, PFQ-AODV provides a flexible, portable, and practicable solution for routing in VANETs. We show the effectiveness of the proposed protocol by using both computer simulations and real-world experiments.