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).

A survey on computation resource allocation in IoT enabled vehicular edge computing

“…This algorithm will create a considerable overhead. To reduce the complexity and overhead, some variables in the P1 are removed using approx- Cloud computing [28,30,33,34,37,39,58] imation, and then the optimization problem comprises only one variable, which can be optimized using the Newton-Raphson convex optimization algorithm.…”

“…Power/energy [28][29][30] QoS [33][34][35] Delay [25,26,28,[35][36][37][38][39] QoE and user satisfaction [34,37] Network/system utility [26,33,37,39,40] Reliability [20] However, at the same time, they are required to perform many intensive computations as part of the network [27]. Hence, authors in [28][29][30][31] try to optimize energy and power consumption in their resource allocation schemes.…”

“…As in any network, revenue generation is directly dependent on the network users, and it is crucial to have a good QoS. In works [33][34][35] focus on maximizing their QoS in their resource allocation schemes for VEC networks.…”

“…In [35], the authors adopt a deep Q-learning based approach in their MEC-based IoV system for optimized RA while simultaneously maintaining QoS. In [34] the authors systematically look into the QoS guaranteed optimal RA problem for connected vehicular fleets in MEC environ-ments. To demonstrate the working of their framework, they take two large real-world datasets of online-taxi services and evaluate their QoS with their framework.…”

“…Ensuring high QoE (quality of experience) or user satisfaction is significant for the networks to ensure adequate participation from users. Studies [34,37] focus on ensuring a good QoE and user satisfaction while implementing their resource allocation frameworks.…”