Energy-Efficient Vehicular Task Offloading Using Multi-Mode MEC and RIS-Equipped Aerial Platforms

Connected and Autonomous Vehicles (CAVs) require ultra-low latency and high computational processing for safety-critical applications, often exceeding their on-board capabilities and facing significant coverage limitations with existing terrestrial infrastructure. To address these challenges, mobile edge computing (MEC)-equipped non-terrestrial networks (NTNs) offer a promising solution for vehicular task offloading. In this context, we introduce here a novel and energy-efficient approach to optimize MEC-equipped NTN operations through the integration of reconfigurable intelligent surfaces (RIS) into NTNs, thus enhancing the performance of CAV task offloading. Our framework leverages a multi-layered cooperative architecture that combines the wide-area coverage of high-altitude platform stations (HAPS) with the flexibility of multi-mode unmanned aerial vehicles (UAVs) equipped with both MEC and RIS capabilities. Specifically, we formulate this as a joint optimization problem of task/sub-task association, RIS phase shift configurations, and power control, to maximize the CAV task offloading success rate while saving energy within the NTN nodes. Given the latter’s NP-hardness, we divide it into three separate sub-problems and solve them iteratively. Specifically, task/sub-task association decisions are addressed by transforming the mixed-integer nonlinear programming (MINLP) sub-problem, and the RIS configurations are optimized using a hybrid solution combining semidefinite programming (SDP) and successive convex approximation (SCA), while a closed-form solution is derived for UAV/HAPS power control. Through extensive experiments, our proposed iterative solution, called joint offloading, phase shift, and power optimization (JOPPO), is proven to be superior to benchmarks in terms of task offloading success rate and across different network conditions while trading-off between energy consumption and task offloading success rate.