A fuzzy logic based clustering strategy for improving vehicular ad-hoc network performance

Abstract

This paper aims to improve the clustering of vehicles by using fuzzy logic in Vehicular Ad-Hoc Networks (VANETs) for making the network more robust and scalable. High mobility and scalability are two vital topics to be considered while providing efficient and reliable communication in VANETs. Clustering is of crucial significance in order to cope with the dynamic features of the VANET topologies. Plenty of parameters related to user preferences, network conditions and application requirements such as speed of mobile nodes, distance to cluster head, data rate and signal strength must be evaluated in the cluster head selection process together with the direction parameter for highly dynamic VANET structures. The prominent parameters speed, acceleration, distance and direction information are taken into account as inputs of the proposed cluster head selection algorithm. The simulation results show that developed fuzzy logic (FL) based cluster head selection algorithm (CHSA) has stable performance in various scenarios in VANETs. This study has also shown that the developed CHSAFL satisfies well the highly demanding requirements of both low speed and high speed vehicles on two-way multilane highway.