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Deep-Reinforcement-Learning-Based Joint Energy Replenishment and Data Collection Scheme for WRSN.

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Summary

This study introduces a novel deep reinforcement learning scheme for wireless rechargeable sensor networks. It optimizes energy replenishment and data collection using unmanned aerial vehicles, improving network efficiency and reducing costs.

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deep reinforcement learningroute protocolunmanned aerial vehicleswireless rechargeable sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless rechargeable sensor networks (WRSNs) enable continuous operation through mobile charging vehicles (MCVs).
  • Existing WRSN strategies often fail to integrate energy replenishment and data collection efficiently.
  • Unmanned aerial vehicles (UAVs) offer high mobility for versatile WRSN applications.

Purpose of the Study:

  • To propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs using deep reinforcement learning.
  • To optimize the integration of data collection and wireless charging processes in WRSNs.
  • To leverage UAV mobility for efficient network operation.

Main Methods:

  • Network partitioning using K-means clustering.
  • Cluster head selection based on remaining energy and location via an improved dynamic routing protocol.
  • Shortest flight path determination using simulated annealing (SA).
  • UAV control and hover point optimization using a multiobjective deep deterministic policy gradient (MODDPG) model.
  • Reward function redesign for joint optimization of node death rate, UAV throughput, and energy consumption.

Main Results:

  • The D-JERDG scheme effectively integrates data collection and energy replenishment.
  • The MODDPG model successfully optimizes UAV flight paths and hover points.
  • The proposed scheme demonstrates significant improvements in throughput, time utilization, and reduced charging costs compared to baseline methods.
  • Joint optimization of multiple objectives including node survival, data throughput, and energy efficiency was achieved.

Conclusions:

  • The D-JERDG scheme offers a robust solution for optimizing WRSN performance.
  • Deep reinforcement learning, specifically MODDPG, is effective for complex UAV-based WRSN management.
  • The proposed approach provides substantial advantages in efficiency and cost-effectiveness for WRSNs.