JOET: Sustainable Vehicle-assisted Edge Computing for Internet of Vehicles

Abstract

Task offloading in Internet of Vehicles (IoV) involves numerous steps and optimization variables such as: where to offload tasks, how to allocate computation resources, how to adjust offloading ratio and transmit power for offloading, and such optimization variables and hybrid combination features are highly coupled with each other. Thus, this is a fully challenge issue to optimize these variables for task offloading to sustainably reduce energy consumption with load balancing while ensuring that a task is completed before its deadline. In this paper, we first provide a Mixed Integer Nonlinear Programming Problem (MINLP) formulation for such task offloading under energy and deadline constraints in IoV. Furthermore, in order to efficiently solve the formulated MINLP, we decompose it into two subproblems, and design a low-complexity Joint Optimization for Energy Consumption and Task Processing Delay (JOET) algorithm to optimize selection decisions, resource allocation, offloading ratio and transmit power adjustment. We carry out extensive simulation experiments to validate JOET. Simulation results demonstrate that JOET outperforms many representative existing approaches in quickly converge and effectively reduce energy consumption and delay. Specifically, average energy consumption and task processing delay have been reduced by 15.93% and 15.78%, respectively, and load balancing efficiency has increased by 10.20%.