Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks
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 as...
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2025-01-01
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| author | Xiaoping Yang Quanzeng Wang Bin Yang Xiaofang Cao |
| author_facet | Xiaoping Yang Quanzeng Wang Bin Yang Xiaofang Cao |
| author_sort | Xiaoping Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-a20606b272cb4b1f899087f51638d688 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-a20606b272cb4b1f899087f51638d6882025-01-24T13:48:45ZengMDPI AGSensors1424-82202025-01-0125239310.3390/s25020393Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor NetworksXiaoping Yang0Quanzeng Wang1Bin Yang2Xiaofang Cao3College of Computer Science, Beijing University of Technology, Beijing 100124, ChinaCollege of Computer Science, Beijing University of Technology, Beijing 100124, ChinaCollege of Computer Science, Beijing University of Technology, Beijing 100124, ChinaSchool of Business, Beijing Wuzi University, Beijing 101149, ChinaUnmanned 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.https://www.mdpi.com/1424-8220/25/2/393unmanned aerial vehiclewireless sensor networkssimultaneously transmitting and reflecting reconfigurable intelligent surfacecomputing offloadingcontent caching |
| spellingShingle | Xiaoping Yang Quanzeng Wang Bin Yang Xiaofang Cao Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks Sensors unmanned aerial vehicle wireless sensor networks simultaneously transmitting and reflecting reconfigurable intelligent surface computing offloading content caching |
| title | Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks |
| title_full | Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks |
| title_fullStr | Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks |
| title_full_unstemmed | Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks |
| title_short | Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks |
| title_sort | energy efficient aerial star ris aided computing offloading and content caching for wireless sensor networks |
| topic | unmanned aerial vehicle wireless sensor networks simultaneously transmitting and reflecting reconfigurable intelligent surface computing offloading content caching |
| url | https://www.mdpi.com/1424-8220/25/2/393 |
| work_keys_str_mv | AT xiaopingyang energyefficientaerialstarrisaidedcomputingoffloadingandcontentcachingforwirelesssensornetworks AT quanzengwang energyefficientaerialstarrisaidedcomputingoffloadingandcontentcachingforwirelesssensornetworks AT binyang energyefficientaerialstarrisaidedcomputingoffloadingandcontentcachingforwirelesssensornetworks AT xiaofangcao energyefficientaerialstarrisaidedcomputingoffloadingandcontentcachingforwirelesssensornetworks |