Open Access

ARTICLE

Energy-Efficient ASTAR-RIS and WPT-Assisted Task Offloading and Content Caching for WSNs

Xiaoping Yang^1,*, Songjie Yang², Junqi Long¹, Quanzeng Wang³, Bin Yang⁴, Xiaofang Cao⁵, Guochao Qi⁶

1 College of Computer Science, Beijing University of Technology, Beijing, China
2 National Key Laboratory of Wireless Communications, University of Electronic Science and Technology of China, Chengdu, China
3 China Unicom Software Research Institute, Beijing, China
4 Center for Strategic Assessment and Consulting, Academy of Military Science, Beijing, China
5 School of Business, Beijing Wuzi University, Beijing, China
6 Beijing Institute of Computer Technology and Application, Beijing, China

* Corresponding Author: Xiaoping Yang. Email:

(This article belongs to the Special Issue: Advances in Wireless Sensor Networks: Security, Efficiency, and Intelligence)

Computers, Materials & Continua 2026, 88(1), 21 https://doi.org/10.32604/cmc.2026.078105

Received 24 December 2025; Accepted 12 February 2026; Issue published 08 May 2026

Abstract

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.

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Keywords

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