Heuristic Based Routing Algorithms for Vehicular Network Using Tabu Search and ANN

Abstract

Efficient routing to guide the vehicles to reach their destinations is a challenging problem in vehicular networks. Many solutions have been proposed in the literature to address the routing problem in vehicular networks. However, most of these solutions are graph-based and do not properly address the dynamic characteristics of vehicular networks. This paper proposes two novel heuristic routing algorithms based on Tabu search and Neural Networks. The proposed algorithms are evaluated and their findings are presented using the UK RTA based roadside dataset. Experimental results along with the comparative analysis made with other related studies are provided to prove the efficiency of the proposed algorithms. The findings highlight the superior performance achieved by the suggested routing algorithms.