A Novel Joint Data Gathering and Wireless Charging Scheme for Sustainable Wireless Sensor Networks

Wu, Qing; Sun, Peng; Boukerche, Azzedine

doi:10.1109/icc40277.2020.9148735

Cited by 8 publications

(8 citation statements)

References 21 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%

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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Section: Related Workmentioning

confidence: 99%

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

Li,

Deng,

Feng

et al. 2024

Sensors

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“…The rationale behind our emphasis on recent and relevant works on CDAC in this survey is its immense popularity among researchers due to its ability to conserve both time and energy within the network. A set of popular works 19,[24][25][26][27] of the joint design have been proposed by utilizing single or multiple MV in wireless sensor network (WSN). That is why in this section, we have included some existing schemes related to CDAC technique which are described as below.…”

Section: Literature Surveymentioning

confidence: 99%

“…This category of research considers that the same MV is used for both CDAC services. Wu et al, 19 have used single MV for data gathering as well as charging at a time. An improved clustering and heuristic‐based scheduling algorithm are constructed to reduce the data gathering delay and maximize charging utility respectively.…”

Section: Literature Surveymentioning

confidence: 99%

“…Several studies [19][20][21][22][23][24] have investigated CDAC using MV, but these approaches have significant drawbacks:…”

Section: Introductionmentioning

confidence: 99%

“…Several studies 19–24 have investigated CDAC using MV, but these approaches have significant drawbacks: Many schemes rely on a single MV to achieve CDAC concurrently, but this approach falls short for large‐scale WRSN. Furthermore, a data gathering and charging schedule that is periodic cannot ensure reduced charging delay and less dead period of sensors.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

Das

Dash

2023

Int J Communication

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SummaryMany cutting‐edge studies on collaborative data gathering and charging (CDAC) assume that the mobile vehicle (MV) has enough energy to charge the sensors as well as collect data from them. The current studies also took into account that the sensors always receive full charge from the MV, resulting in a wireless rechargeable sensor network (WRSN) with very little dead period and very little data gathering latency. It is also believed that the energy consumption rates of the sensors are constant. However, in large‐scale WRSN, the aforementioned considerations are not always practical. In addition, the utilization of single base station (BS) in a large‐scale WRSN cannot guarantee improved network scalability, expedite charging decisions by minimizing the substantial overhead of the BS, and enhance the traveling distance for MV. In order to overcome the aforementioned problems, we present an effective on‐demand partial CDAC scheme using battery‐limited multiple MVs. The proposed scheme implements the CDAC process in such a way that the total dead periods of the sensors and the energy consumption of MVs are reduced. We use a multi‐objective‐based genetic algorithm (GA) to optimize the entire CDAC process. The simulation is carried out to demonstrate the usefulness and competitiveness of the proposed scheme. In comparison with existing works, the proposed work improves CDAC performance by reducing sensor dead time and energy consumption of MV.

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An efficient charging scheme using battery constrained mobile charger in wireless rechargeable sensor networks

2022

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A Novel Joint Data Gathering and Wireless Charging Scheme for Sustainable Wireless Sensor Networks

Cited by 8 publications

References 21 publications

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

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

Collaborative data gathering and recharging using multiple mobile vehicles in wireless rechargeable sensor network

An efficient charging scheme using battery constrained mobile charger in wireless rechargeable sensor networks

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