On-Demand Edge Computing Power Networks Assisted by Reconfigurable Intelligent Surface With Multi-Layer Scheme

On-demand edge computing power networks with both stationary fog nodes co-located with cellular base stations (CFNs) and mobile fog nodes mounted on vehicles (VFNs) provide promising solutions for coping with high spatio-temporal, compute-intensive, and latency-sensitive applications. Joint scheduling and resource allocation in such a network is challenging due to the trade-off between quality of service (QoS) and energy consumption, limited onboard capacity of IoT devices and fog nodes, and urban obstructions that impede line-of-sight links. To address these issues, this work envisions a network assisted by reconfigurable intelligent surface (RIS) with a multi-layer scheme. The computation tasks are offloaded from IoT devices to VFNs and further to CFNs based on the computational demand and latency requirements, and the RIS assists with wireless communication on both links. We jointly optimized the allocation of the subcarriers, the power, the offloading task bits, the time slot, and the RIS beamforming vectors under the constraints of task input bits and computing capability, to minimize the average energy consumption. To address the non-convex issue, we first decompose it into three sub-problems, and then alternately optimize these sub-problems by adopting successive convex approximation (SCA) where a locally optimal solution can be obtained. Simulation results demonstrate the superiority of the proposed offloading strategy where RIS with a multi-layer scheme is introduced in the on-demand edge computing power networks. Also, the effectiveness, feasibility, scalability, and adaptability of the designed algorithm are verified.