A Hybrid Congestion Detection Based Mobility Model for Vehicular Adhoc Networks

Vehicular Adhoc Network (VANET) is a challenging and growing technology for smart vehicles.It is a network of connected nodes called vehicles.Sharing of information takes place between vehicle to vehicle, vehicle to roadside unit (RSU), RSU to RSU.Various network protocols govern the communication in VANET.Two intensions of VANET are sharing some critical information like road blockage, accidents on road, information about traffic jams etc among vehicles within a particular range and availing entertainment facilities like chatting, toll information, video streaming etc during a journey.Here the communication is either between vehicle to vehicle or between vehicles to infrastructure.It is seen in survey that, due to traffic congestion there is a huge loose of fuel and valuable time.The time spent in traffic congestion can be spent in some productive works.The rapid growing technology of Internet of Things (IoT) made it possible to detect road congestion smartly before it stuck the vehicle in the congestion, so that the vehicle can be diverted in some other route to their destination.This paper proposes two modules one for congestion detection ANFIS based Hybrid Congestion Detection Based Mobility Model (CDMM) that makes aware the vehicle about the congestion on the route and followed by second modified ACO (Ant Colony optimization) module to follow an optimum route to the destination.Also PSO ( Particle Swarm Optimization ) algorithm is implemented to find optimal route to the destination.Both the modified ACO and PSO algorithms are compared to use the best one.After getting information about congestion a vehicle may transfer the same information to other vehicles to prohibit them to enter and get stuck into the jams.