Fuzzy-assisted social-based routing for urban vehicular environments

Abstract

In the autonomous environment of Vehicular Ad hoc NETwork (VANET), vehicles randomly move with high speed and rely on each other for successful data transmission process. The routing can be difficult or impossible to predict in such intermittent vehicles connectivity and highly dynamic topology. The existing routing solutions do not consider the knowledge that behaviour patterns exist in real-time urban vehicular networks. In this article, we propose a fuzzy-assisted social-based routing (FAST) protocol that takes the advantage of social behaviour of humans on the road to make optimal and secure routing decisions. FAST uses prior global knowledge of real-time vehicular traffic for packet routing from the source to the destination. In FAST, fuzzy inference system leverages friendship mechanism to make critical decisions at intersections which is based on prior global knowledge of realtime vehicular traffic information. The simulation results in urban vehicular environment for with and without obstacles scenario show that the FAST performs best in terms of packet delivery ratio with upto 32% increase, average delay 80% decrease, and hops count 50% decrease compared to the state of the art VANET routing solutions.