Dynamic Charging Strategy Optimization for UAV-Assisted Wireless Rechargeable Sensor Networks Based on Deep <i>Q</i>-Network

Liu, Ning; Zhang, Jian; Luo, Chuanwen; Cao, Jia; Hong, Yi; Chen, Zhibo; Chen, Ting

doi:10.1109/jiot.2023.3345311

Cited by 1 publication

(2 citation statements)

References 27 publications

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“…In this section, we provide a brief overview of the existing work in three relevant domains: cluster-based networks [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ], traditional algorithm-based UAV trajectory planning [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ], and DRL-based UAV trajectory planning [ 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Related Workmentioning

confidence: 99%

“…In their study, UAVs served as mobile chargers to replenish energy for nodes, while mobile vehicles acted as mobile base stations to replace UAV batteries. The authors utilized a multiobjective deep Q-network (DQN) algorithm to minimize sensor downtime and optimize UAV energy consumption [ 42 ]. Wang et al proposed a dynamic spatiotemporal charging scheduling scheme based on deep reinforcement learning, given the discrete charging sequence planning and continuous charging duration adjustment in mobile charging scheduling, to improve the charging performance while avoiding the power death of nodes [ 43 ].…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

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

Li,

Deng,

Feng

et al. 2024

Sensors

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With the emergence of wireless rechargeable sensor networks (WRSNs), the possibility of wirelessly recharging nodes using mobile charging vehicles (MCVs) has become a reality. However, existing approaches overlook the effective integration of node energy replenishment and mobile data collection processes. In this paper, we propose a joint energy replenishment and data collection scheme (D-JERDG) for WRSNs based on deep reinforcement learning. By capitalizing on the high mobility of unmanned aerial vehicles (UAVs), D-JERDG enables continuous visits to the cluster head nodes in each cluster, facilitating data collection and range-based charging. First, D-JERDG utilizes the K-means algorithm to partition the network into multiple clusters, and a cluster head selection algorithm is proposed based on an improved dynamic routing protocol, which elects cluster head nodes based on the remaining energy and geographical location of the cluster member nodes. Afterward, the simulated annealing (SA) algorithm determines the shortest flight path. Subsequently, the DRL model multiobjective deep deterministic policy gradient (MODDPG) is employed to control and optimize the UAV instantaneous heading and speed, effectively planning UAV hover points. By redesigning the reward function, joint optimization of multiple objectives such as node death rate, UAV throughput, and average flight energy consumption is achieved. Extensive simulation results show that the proposed D-JERDG achieves joint optimization of multiple objectives and exhibits significant advantages over the baseline in terms of throughput, time utilization, and charging cost, among other indicators.

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