An Improved Genetic Based Routing Protocol for VANETs

Abstract

Vehicular Ad-hoc Network (VANET) is a most critical class of mobile ad-hoc network (MANET) that enables roadside vehicles to intelligently interact with one another and with outside infrastructure anytime anywhere in the global network. VANETs are self configuring network where nodes are vehicle and WIFI technologies are used to establish these networks. Routing is the process of sending data packets from source node to destination node with the minimum cost. Routing algorithm organizes and distributes information in the network. In genetic algorithm, any path from starting node to target node is a feasible solution and best possible solution is the smallest path from starting to target. In the beginning a random population of string is generated, this is the string of nodes which represents feasible (admissible) and unfeasible (un-admissible) solution. The dissertation focuses on routing in VANETs. In this dissertation, genetic algorithm is applied to improve the routing. Genetic algorithm finds the highly optimal (shortest) path for the entire network. A routing tree is formed from fitness function that provides highly optimized and fault tolerant routing. A routing tree is also constructed that allow efficient insertion and deletion of the node. The method was observed for packet delivery ratio, throughput, delay and packet loss. The proposed algorithm which is based on the integration of GA with existing routing protocol allows formation of efficient network with more fault tolerant and enhanced robustness.