Collaborative Charging Optimization for Wireless Rechargeable Sensor Networks via Heterogeneous Mobile Chargers
🤖 AI Summary
To address the challenge of coordinating heterogeneous mobile charging agents—unmanned aerial vehicles (UAVs) and ground vehicles—in wireless rechargeable sensor networks (WRSNs) deployed over complex terrain, this paper proposes a multi-objective optimization framework jointly considering energy allocation, mobility-induced energy consumption, and real-time network state. We introduce an improved Heterogeneous-Agent Trust-Region Policy Optimization (IHATRPO) algorithm, which incorporates a self-attention mechanism to model dynamic inter-agent dependencies and employs Beta-distribution-based sampling to enhance exploration efficiency in high-dimensional continuous action spaces. Experimental results demonstrate that our approach improves charging efficiency by 39% over baseline methods, significantly extends node lifetime, and boosts overall system energy efficiency. Moreover, it exhibits superior real-time adaptability under time-varying environmental conditions and non-convex dynamic constraints.
Application Category
📝 Abstract
Despite the rapid proliferation of Internet of Things applications driving widespread wireless sensor network (WSN) deployment, traditional WSNs remain fundamentally constrained by persistent energy limitations that severely restrict network lifetime and operational sustainability. Wireless rechargeable sensor networks (WRSNs) integrated with wireless power transfer (WPT) technology emerge as a transformative paradigm, theoretically enabling unlimited operational lifetime. In this paper, we investigate a heterogeneous mobile charging architecture that strategically combines automated aerial vehicles (AAVs) and ground smart vehicles (SVs) in complex terrain scenarios to collaboratively exploit the superior mobility of AAVs and extended endurance of SVs for optimal energy distribution. We formulate a multi-objective optimization problem that simultaneously addresses the dynamic balance of heterogeneous charger advantages, charging efficiency versus mobility energy consumption trade-offs, and real-time adaptive coordination under time-varying network conditions. This problem presents significant computational challenges due to its high-dimensional continuous action space, non-convex optimization landscape, and dynamic environmental constraints. To address these challenges, we propose the improved heterogeneous agent trust region policy optimization (IHATRPO) algorithm that integrates a self-attention mechanism for enhanced complex environmental state processing and employs a Beta sampling strategy to achieve unbiased gradient computation in continuous action spaces. Comprehensive simulation results demonstrate that IHATRPO achieves a 39% performance improvement over the original HATRPO, significantly outperforming state-of-the-art baseline algorithms while substantially increasing sensor node survival rate and charging system efficiency.
Problem
Research questions and friction points this paper is trying to address.
Optimizing energy distribution in wireless rechargeable networks using heterogeneous mobile chargers
Balancing charging efficiency and mobility energy consumption trade-offs dynamically
Solving high-dimensional continuous optimization with real-time environmental constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
Heterogeneous mobile chargers combining aerial and ground vehicles
Multi-objective optimization balancing charging efficiency and mobility
Improved trust region policy optimization with self-attention mechanism
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