Optimizing Energy and Revenue Efficiency in UAV-Assisted Vehicular Networks With Enhanced Reward Twin Actor TD3 and Secure Caching Strategies

Abstract

Unstable transmission conditions degrade the offloading performance of Mobile Edge Computing (MEC) servers in vehicular communication environments. Cache-enabled uncrewed aerial vehicles (UAVs) with computation and caching capabilities offer a potential solution by acting as mobile edge servers in the air. Due to superior channel conditions, UAVs provide more reliable communication than traditional ground servers in vehicular networks. Additionally, caching popular task results in UAV storage improves service efficiency. However, challenges remain in designing offloading schemes that balance energy consumption, service revenue, and secure caching. This paper formulates a joint optimization problem based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to determine UAV trajectory, offloading schemes, and UAV-side resource allocation. The objective is to minimize energy consumption and maximize service revenue in secure cache-enabled UAV-assisted vehicular networks under delay and resource constraints. To tackle the high nonlinearity and intricate action space, we propose a novel Twin Actor Network combined with a hierarchical reward mechanism, together forming the Enhanced Reward Twin Actor Twin Delayed Deep Deterministic Policy Gradient (ERTATD3) framework. Simulation results show that the proposed algorithm outperforms existing methods in terms of convergence, reward, caching security, delay, energy efficiency, and revenue optimization.