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Deep Reinforcement Learning-Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network.

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This study introduces an efficient one-to-multiple charging scheme for wireless rechargeable sensor networks (WRSN) using Deep Reinforcement Learning. The novel approach optimizes mobile charger scheduling to minimize node failures and reduce charging latency in large-scale networks.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless rechargeable sensor networks (WRSN) address energy constraints in wireless sensor networks (WSN).
  • Existing one-to-one mobile charging (MC) schemes struggle with the high energy demands of large-scale WSNs.
  • One-to-multiple charging offers a more scalable solution for efficient energy replenishment.

Purpose of the Study:

  • To develop an online one-to-multiple charging scheme for large-scale WSNs.
  • To optimize mobile charger scheduling and node charging amounts using Deep Reinforcement Learning.
  • To minimize network downtime and ensure timely energy replenishment.

Main Methods:

  • Proposed an online one-to-multiple charging scheme leveraging Deep Reinforcement Learning (DRL).
  • Utilized Double Dueling Deep Q-Network (3DQN) to jointly optimize MC charging sequence and node charging amounts.
  • Implemented network cellularization based on MC effective charging distance.
  • Adjusted charging amounts based on node energy demand, network survival time, and MC residual energy.

Main Results:

  • The proposed 3DQN scheme demonstrated superior charging performance compared to existing methods.
  • Achieved significant reductions in the node dead ratio.
  • Showcased substantial improvements in minimizing charging latency.
  • The DRL approach enhanced training stability and reduced overestimation for better adaptability.

Conclusions:

  • The online one-to-multiple charging scheme effectively addresses energy replenishment challenges in large-scale WSNs.
  • DRL, specifically 3DQN, provides an optimal strategy for mobile charging scheduling and energy distribution.
  • The proposed method offers a robust and efficient solution for extending network lifetime and performance.