Research and Design of an Optimal Management Strategy for Simultaneous Wireless Power and Information Transmission for Highway Vehicles

Mobile edge computing (MEC) provides a new solution for meeting the growing energy and computational demands of road transportation systems. However, it is difficult for MEC alone to satisfy the high associated computational requirements. Therefore, a cloud-assisted mobile edge computing (CAMEC) framework is used in this paper to investigate the problem of computation and offloading strategies for tasks. First, to address the cost of roadside unit (RSU) deployment, the use of vehicles being charged (VCs) as edge servers is proposed. Additionally, to minimize the system latency, this study considers the problem of maximizing the channel capacity for the simultaneous wireless transmission of power and information (SWTPI). Based on this, a computational offloading model is developed to minimize a weighted sum of system delay and energy consumption, with the available server and device resources and the maximum delay as constraints. To solve this multivariate nonconvex problem, an iterative algorithm based on successive convex approximations and alternating iterations is proposed. Simulation results show that under the offloading scheme proposed in this paper, the system cost is reduced by approximately 30% compared to that with MEC only, indicating that the scheme is effective at reducing the weighted sum of delay and energy consumption.