AN ENERGY AWARE OPTIMIZED INTELLIGENT TRANSPORT SYSTEM USING ELHACO WITH FUZZY METHOD TO CONTROL TRAFFIC IN INTERNET OF VEHICLES

The Internet of Vehicle (IoV) utilizes networks to conduct message exchange and related services or application. In recent years, smart cities and IoVs have become areas of interest in the new generation Internet of Things development, especially since the development of intelligent transportation system has focused on bettering traf ic conditions. Continuous topological changes of vehicular communications are a significant issue in IoV that can af ect the change in network scalability, and the shortest routing path. Therefore, organizing efficient and reliable intercommunication routes between vehicular nodes, based on conditions of traf ic intensity is an increasingly challenging issue. For such issues, earlier Fuzzy logic based traf ic intensity calculation function is proposed in this paper to model the heavy. However, Fuzzy logic is not always accurate, so the results of traf ic intensity are perceived based on assumption, so it may not be widely putative.So, this workproposed a novel intelligent system-based algorithm is proposed asEnergy-aware routing scheme based on Lively Hovering Ant Colony Optimization (ELHACOIoV) to enhance the outcome of optimal route path selection. Another algorithm, called Gaussian based Fuzzy neural network with Genetic Algorithm (GFNNGA), is employed together with ELHACOIoV and referred as ELHACOGFNNGAIoVfor the adaptation of transmission range regarding of intensity in local traf ic. The results presented through NS-2 simulations show that the new protocol is superior to existing EACOFNNIoV, Ad hoc On-demand Distance Vector (AODV) routing and (ACO) protocols based on evaluating routing performance in terms of throughput, packet delivery, and drop ratio and average end-to-end delay