A Deep Reinforcement Learning-based Adaptive Charging Policy for WRSNs
This work addresses the challenge of maintaining continuous surveillance in wireless sensor networks by improving charging efficiency, though it is incremental as it builds on existing DRL methods for a specific domain.
The paper tackles the problem of designing an optimal charging path for a mobile charger in wireless sensor networks under uncertainties like fluctuating energy consumption and node failures, proposing a deep reinforcement learning-based adaptive charging scheme that outperforms existing on-demand algorithms by a significant margin.
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.