Energy-and-delay-aware scheduling and load balancing in vehicular fog networks

Abstract

Roadside units (RSUs) play an important role in the request fulfillment of vehicles. In rural areas, RSUs are powered by renewable energy sources like solar energy. Hence, the request fulfillment of vehicles must be done in such a way that the energy consumption across RSUs is minimized. The requests are categorized into traditional application (low computation) requests and smart application (high computation) requests. To avoid excessive computation at RSUs, low computation requests are scheduled across RSUs while high computation requests are scheduled across fog servers for processing. In this paper, we propose an online energy-efficient Inter-RSU Scheduling Algorithm (ee-IRSA) and a distributed Ant Colony Optimization-based Load balancing technique (d-ACOL) for optimizing the request fulfillment of traditional and smart application requests, respectively. ee-IRSA ensures minimum and uniform energy consumption across RSUs while d-ACOL ensures minimum queue waiting time of requests across fog servers. In addition, a second-price auction game-based relay vehicle selection technique is proposed which further minimizes the energy consumption of RSUs. Simulation results show that ee-IRSA with relay vehicle selection reduces the energy consumption by 33%, and d-ACOL reduces the queue waiting time by an average of 48% as compared to other load balancing techniques.