Energy Efficiency Optimization in Vehicle Communication Network Using Hybrid-Genetic Algorithm

Abstract

In the current world of Intelligent Transportation System (ITS), Vehicle Communication Networks (VCNs) have a critical function of facilitating the communication of various ITS entities in vehicles, their environment, and other interconnected entities. Unfortunately, the dynamic and resource-constrained nature of VCNs creates major issues in the energy efficiency area, which is extremely important in extending the usable life of the network in those applications that incorporate battery-operated devices and/or mobile nodes. In this paper, a novel heuristic solution for the enhancement of the energy efficiency of VCNs is proposed and implemented using a Hybrid-Genetic Algorithm (HGA). The proposed algorithm combines the advantage of the normal Genetic Algorithms (GA) of a large search space while using local search to fine-tune results and result in fast convergence towards the optimum routing path. The goal is to reduce the overall energy expended in transmitting data, receiving data, and then processing this data while meeting reliability, delay, and throughput measures of Quality of Service (QoS). The proposed HGA achieved a 21% reduction in energy consumption compared to the Standard GA and a 13.6% reduction compared to Simulated Annealing, while maintaining high QoS standards. Simulation results are presented, which show the proposed algorithm incorporates balance between exploitative and explorative approaches such that routing paths in dynamic, challenging, and vehicular environments are achieved optimally. This research benefits the creation of sustainable VCNs that aid in the other objectives regarding efficiency in energy use and durability in ITS.