New clustering algorithms for vehicular ad-hoc network in a highway communication environment

A vehicular ad hoc network (VANET) is a network in which vehicles acting as dynamic nodes communicate with each other. A VANET is a suitable piece of infrastructure for developing intelligent transportation systems. Stable communication within a VANET leads to enhanced driver safety and better traffic management. The clustering technique, which organizes similar vehicles into similar groups, is a possible method for improving the stability of connectivity within a VANET. In this paper, two new clustering algorithms suited to the dynamic environment of a VANET are proposed. The multi-objective data envelopment analysis clustering algorithm as a mathematical clustering model and the ant system-based clustering algorithm as a meta-heuristic clustering model are introduced as algorithms for VANETs. A comparative simulation study in a highway environment is presented as well to evaluate the introduced methods and compare them with the most commonly used VANET clustering algorithms. The results show that the proposed algorithms offer improved stability and runtime along with relatively better performance than existing algorithms. Furthermore, the results show that in the VANET environment, the mathematical clustering model proposed herein yields better results than the meta-heuristic algorithm.