发表机构
Imam Khomeini International University; Amirkabir University of Technology（伊玛目霍梅尼国际大学; 阿米尔卡比尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对WSN能量不均衡问题，提出统一移动智能体框架，以分层强化学习顺序执行数据采集与无线充电，实现自适应调度，延长网络寿命15%并均衡能量分布。
AI 中文摘要
能量不均衡仍然是无线传感器网络（WSN）中的一个关键挑战，因为靠近基站的节点由于承担较重的转发负载而更快耗尽能量。虽然移动智能体（MAs）已被用于数据采集或传感器充电，但现有方法缺乏适应性，并且未能在现实硬件约束下整合这两种功能。本文引入了一个统一的移动智能体框架，在单天线限制下顺序执行数据采集和无线充电。智能体的决策被建模为一个两层分层强化学习（HRL）问题，其中上层优化移动规划，下层根据实时网络状态确定合适的服务。这种分层结构使智能体能够学习自适应任务调度策略，而无需预定义规则。大量仿真表明，与最先进的移动智能体和深度强化学习方法相比，所提出的方法实现了长达15%的网络寿命延长和更均衡的能量分布。
英文摘要
Energy imbalance remains a key challenge in Wireless Sensor Networks (WSNs), as nodes near the base station deplete their energy faster due to heavy forwarding loads. While mobile agents (MAs) have been employed for either data collection or sensor charging, existing approaches lack adaptability and fail to integrate both functions under realistic hardware constraints. This paper introduces a unified mobile agent framework that performs both data collection and wireless charging sequentially under single-antenna limitations. The agent's decision-making is formulated as a two-layer Hierarchical Reinforcement Learning (HRL) problem, where the upper layer optimizes movement planning and the lower layer determines the appropriate service based on real-time network states. This hierarchical structure enables the agent to learn adaptive task scheduling policies without predefined rules. Extensive simulations demonstrate that the proposed method achieves up to 15% longer network lifetime and more balanced energy distribution compared with state-of-the-art mobile agent and deep RL approaches.
