Location-based data delivery between vehicles and infrastructure

Abstract

Multi-hop routing in vehicular ad-hoc networks (VANETs) and wireless sensor networks has attracted significant interest of researchers in the wireless ad-hoc networks community. Most multi-hop routing protocols in VANET are based around the idea of choosing the next destination, which will provide the shortest-delay to reach a destination. To ensure better monitoring and reporting of road condition information, this study proposes location-based data forwarding through roadside sensors using k-shortest path routing combined with Q-learning. Q-learning is used for exploration of the sensing field to determine those sensors which have a higher queuing delay during peak hours as well as those which have comparatively lower delays. The use of Q-learning for exploration (sans routing) enables faster convergence for the sensors as compared to those techniques which utilise naive Q-learning for shortest path routing. Secondly, multi-hop routing is being combined with source coding (Huffman and Arithmetic coding) to compress the data payload of packets. This has shown some promising results for the VANETs employing dedicated short-range communication.