Unmanned aerial vehicle (UAV)-based wireless sensor networks (WSNs) hold great promise for supporting ground-based sensors due to the mobility of UAVs and the ease of establishing line-of-sight links. UAV-based WSNs equipped with mobile edge computing (MEC) servers effectively mitigate challenges associated with long-distance transmission and the limited coverage of edge base stations (BSs), emerging as a powerful paradigm for both communication and computing services. Furthermore, incorporating simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) as passive relays significantly enhances the propagation environment and service quality of UAV-based WSNs. However, most existing studies place STAR-RISs in fixed positions, ignoring the flexibility of STAR-RISs. Some other studies equip UAVs with STAR-RISs, and UAVs act as flight carriers, ignoring the computing and caching capabilities of UAVs. To address these limitations, we propose an energy-efficient aerial STAR-RIS-aided computing offloading and content caching framework, where we formulate an energy consumption minimization problem to jointly optimize content caching decisions, computing offloading decisions, UAV hovering positions, and STAR-RIS passive beamforming. Given the non-convex nature of this problem, we decompose it into a content caching decision subproblem, a computing offloading decision subproblem, a hovering position subproblem, and a STAR-RIS resource allocation subproblem. We propose a deep reinforcement learning (DRL)–successive convex approximation (SCA) combined algorithm to iteratively achieve near-optimal solutions with low complexity. The numerical results demonstrate that the proposed framework effectively utilizes resources in UAV-based WSNs and significantly reduces overall system energy consumption.

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms.

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.