A Deep Reinforcement Learning-based Adaptive Charging Policy for WRSNs

TL;DR

This paper introduces a deep reinforcement learning-based adaptive charging policy for wireless sensor networks, enabling mobile chargers to efficiently recharge sensors amid unpredictable network changes, improving over existing methods.

Contribution

The paper presents a novel DRL-based adaptive charging scheme that dynamically adjusts to network topology changes, enhancing recharging efficiency in WRSNs.

Findings

Outperforms existing on-demand algorithms significantly

Adapts to spontaneous network topology changes

Uses deep neural networks to optimize charging paths

Abstract

Wireless sensor networks consist of randomly distributed sensor nodes for monitoring targets or areas of interest. Maintaining the network for continuous surveillance is a challenge due to the limited battery capacity in each sensor. Wireless power transfer technology is emerging as a reliable solution for energizing the sensors by deploying a mobile charger (MC) to recharge the sensor. However, designing an optimal charging path for the MC is challenging because of uncertainties arising in the networks. The energy consumption rate of the sensors may fluctuate significantly due to unpredictable changes in the network topology, such as node failures. These changes also lead to shifts in the importance of each sensor, which are often assumed to be the same in existing works. We address these challenges in this paper by proposing a novel adaptive charging scheme using a deep reinforcement learning (DRL) approach. Specifically, we endow the MC with a charging policy that determines the next sensor to charge conditioning on the current state of the network. We then use a deep neural network to parametrize this charging policy, which will be trained by reinforcement learning techniques. Our model can adapt to spontaneous changes in the network topology. The empirical results show that the proposed algorithm outperforms the existing on-demand algorithms by a significant margin.