Collaborative Charging Optimization for Wireless Rechargeable Sensor Networks via Heterogeneous Mobile Chargers

Table of Contents

• 1. Introduction
• 2. Methodology
  • 2.1 Heterogeneous Charger Architecture
  • 2.2 Problem Formulation
  • 2.3 IHATRPO Algorithm
• 3. Technical Implementation
  • 3.1 Mathematical Framework
  • 3.2 Algorithm Details
• 4. Experimental Results
  • 4.1 Performance Metrics
  • 4.2 Comparative Analysis
• 5. Code Implementation
• 6. Future Applications
• 7. References

1. Introduction

Wireless Rechargeable Sensor Networks (WRSNs) represent a transformative paradigm that integrates wireless power transfer (WPT) technology with conventional sensing capabilities, theoretically enabling unlimited operational lifetime for IoT applications. Traditional WSNs face persistent energy limitations that severely constrain network lifetime and operational sustainability.

2. Methodology

2.1 Heterogeneous Charger Architecture

The proposed architecture combines Automated Aerial Vehicles (AAVs) and Ground Smart Vehicles (SVs) to exploit their complementary advantages in complex terrain scenarios. AAVs provide superior mobility and rapid deployment, while SVs offer extended endurance and higher power capacity.

2.2 Problem Formulation

The multi-objective optimization problem addresses:

• Dynamic balance of heterogeneous charger advantages
• Charging efficiency versus mobility energy consumption trade-offs
• Real-time adaptive coordination under time-varying network conditions

2.3 IHATRPO Algorithm

The Improved Heterogeneous Agent Trust Region Policy Optimization (IHATRPO) algorithm integrates self-attention mechanisms for complex environmental state processing and employs Beta sampling strategy for unbiased gradient computation in continuous action spaces.

3. Technical Implementation

3.1 Mathematical Framework

The optimization problem is formulated as maximizing the network utility function:

$U = \sum_{i=1}^{N} \log(1 + E_i^{charged}) - \lambda \sum_{j=1}^{M} C_j^{mobility}$

where $E_i^{charged}$ represents energy delivered to sensor node i, $C_j^{mobility}$ denotes mobility cost of charger j, and $\lambda$ is the trade-off parameter.

3.2 Algorithm Details

IHATRPO extends the Trust Region Policy Optimization framework with:

• Self-attention mechanisms for processing complex state representations
• Beta distribution sampling for continuous action spaces
• Heterogeneous agent coordination through centralized training with decentralized execution

4. Experimental Results

4.1 Performance Metrics

4.2 Comparative Analysis

The proposed IHATRPO algorithm significantly outperforms state-of-the-art baseline algorithms including DQN, PPO, and original HATRPO across multiple metrics including charging efficiency, energy consumption, and network coverage.

5. Code Implementation

Pseudocode for IHATRPO algorithm:

Initialize policy parameters θ, value function parameters φ
for iteration=1,2,... do
    Collect trajectory set D using policy π_θ
    Compute advantage estimates Â_t using GAE
    Update policy by maximizing objective:
        L(θ) = E[min(r_t(θ)Â_t, clip(r_t(θ), 1-ε, 1+ε)Â_t)]
    Update value function by regression on V_φ
    Update self-attention weights for state processing
end for

6. Future Applications

The proposed heterogeneous charging architecture has promising applications in:

• Smart city infrastructure monitoring
• Industrial IoT and automation systems
• Environmental monitoring in remote areas
• Disaster response and emergency networks
• Agricultural automation and precision farming

7. References

1. J. Yao et al., "Collaborative Charging Optimization for WRSNs via Heterogeneous Mobile Chargers," IEEE Transactions.
2. D. Niyato, "Wireless Charging Technologies: Principles and Applications," IEEE Communications Surveys & Tutorials, 2022.
3. J. Schulman et al., "Trust Region Policy Optimization," ICML 2015.
4. A. Vaswani et al., "Attention Is All You Need," NeurIPS 2017.
5. L. Xie et al., "Wireless Power Transfer and Energy Harvesting: Current Status and Future Directions," Proceedings of the IEEE, 2023.

Expert Analysis