Intelligent resource allocation for utility optimization in RSU-empowered vehicular network

Abstract

Intelligent transportation system (ITS) has attracted extensive attention in both academia and industry for its potential benefits. For example, ITS is dedicated to convenient, economical and environmentally friendly service provisioning for the drivers and passengers in vehicles via advanced technologies including artificial intelligence (AI), knowledge mining, depth fusion, etc. Besides, several newly emerging computing paradigms revolved around ITS such as vehicular cloud and vehicular fog computing are proposed to fully exploit idle computing and communication resources within connected vehicles. As the number of vehicular applications is explosively increasing, it has posed great challenges to the limited capabilities of vehicle loaded computer systems and communication facility. Accordingly, more intelligent resource allocation strategies are needed for computationally intensive and time sensitive vehicular applications. In this paper we propose a road side unit (RSU) empowered vehicular network that consists of three hierarchical layers-vehicular cloud, RSU-enabled cloudlet, and central cloud, respectively. RSU is enhanced with edge servers such that it can intelligently respond to the resource requests in a real time fashion. To this end, an approximate but efficient resource allocation strategy is proposed that can intelligently optimize the utility value from the perspective of RSU-enabled cloudlet. Extensive experiments are carried out to evaluate the performance of the strategy. The results reveal that the proposed algorithm DbHA shows great advantages over other approaches such as the genetic algorithm (GA) and particle swarm optimization (PSO) in both respects (i.e., performance and response latency).