A D2D and RIS-Assisted Task Offloading Method Based on an Improved Fox Optimization Algorithm

With the progress of industrial Internet of Things and communication technology, task offloading in edge computing has attracted more and more attention. Aiming at the impact of obstacles on task offloading performance in the process of device offloading in industrial scenarios and the problem of high communication costs of network edge devices, we propose a task offloading scheme assisted by combined Device to Device and Reconfigurable Intelligent Surface. By jointly optimizing computing resources, task offloading decisions, RIS phase shifting and industrial device transmission power, a balanced total energy consumption and latency cost optimization problem is established. We propose an approach using the Improved Fox Optimization Algorithm (IFOA) to maximize system revenue. Extensive simulation data demonstrates that compared with traditional optimization algorithms, it has better comprehensive performance.