The rapid proliferation of latency-sensitive applications, coupled with the limitations of service range, has driven the integration of aerial simultaneously transmitting and reflecting reconfigurable intelligent surfaces (ASTAR-RIS) and task offloading to enhance both communication and computational efficiency in wireless sensor networks (WSNs). However, in WSNs, conventional ASTAR-RIS-assisted task offloading faces critical limitations, including restricted endurance, underutilized network caching and computing resources, and inefficient resource allocation within the optimization framework. To overcome these challenges, this paper integrates wireless power transfer (WPT) technology and proposes a novel energy-efficient ASTAR-RIS and WPT-assisted task offloading and content caching framework for WSNs. Furthermore, we construct a minimization problem that jointly optimizes content caching, energy harvesting time, task offloading, and STAR-RIS resource allocation decisions to minimize energy consumption. Due to its inherently non-convex structure, the problem is addressed by separating it into four subproblems involving content caching, energy harvesting time, task offloading, and STAR-RIS resource allocation decisions. To address the above subproblems, a joint deep reinforcement learning (DRL)–successive convex approximation (SCA) based scheme is designed, which iteratively achieves the solution and attains near-optimal performance with relatively low computational complexity. Simulation results show that the proposed framework achieves more efficient resource utilization in WSNs and markedly lowers the total energy consumption of the system.
Yang et al. (Thu,) studied this question.