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Energy-Efficient Aerial STAR-RIS-Aided Computing Offloading and Content Caching for Wireless Sensor Networks.

Xiaoping Yang1, Quanzeng Wang1, Bin Yang1

  • 1College of Computer Science, Beijing University of Technology, Beijing 100124, China.

Sensors (Basel, Switzerland)
|January 25, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an energy-efficient framework for Unmanned Aerial Vehicle (UAV)-based wireless sensor networks (WSNs) using reconfigurable intelligent surfaces. The approach optimizes computing offloading and content caching, significantly reducing system energy consumption.

Keywords:
computing offloadingcontent cachingsimultaneously transmitting and reflecting reconfigurable intelligent surfaceunmanned aerial vehiclewireless sensor networks

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Area of Science:

  • Wireless Communication
  • Network Engineering
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicle (UAV)-based wireless sensor networks (WSNs) offer mobility and line-of-sight links for ground sensors.
  • Mobile Edge Computing (MEC) in UAV-WSNs addresses transmission and coverage limitations of traditional base stations.
  • Simultaneously Transmitting and Reflecting Reconfigurable Intelligent Surfaces (STAR-RISs) enhance propagation but are often fixed or underutilized.

Purpose of the Study:

  • To propose an energy-efficient framework for UAV-based WSNs integrating STAR-RIS for computing offloading and content caching.
  • To address limitations of fixed STAR-RIS positions and underutilized UAV computing/caching capabilities.
  • To minimize overall system energy consumption through joint optimization.

Main Methods:

  • Formulation of an energy consumption minimization problem.
  • Decomposition into subproblems: content caching, computing offloading, UAV hovering, and STAR-RIS resource allocation.
  • Application of a Deep Reinforcement Learning (DRL)-Successive Convex Approximation (SCA) combined algorithm for near-optimal solutions.

Main Results:

  • The proposed framework effectively optimizes resource utilization in UAV-based WSNs.
  • Significant reduction in overall system energy consumption demonstrated.
  • Numerical results validate the efficiency of the DRL-SCA algorithm.

Conclusions:

  • The aerial STAR-RIS-aided framework offers a flexible and efficient solution for UAV-WSNs.
  • Joint optimization of caching, offloading, positioning, and beamforming is crucial for energy efficiency.
  • The DRL-SCA approach provides a low-complexity method for solving complex optimization problems in this domain.