An Efficient Charging Algorithm for UAV-aided Wireless Sensor Networks

Yang, Longan; Su, Zhiyuan; Yang, H.; Na, Zhixiong; Yan, Feng

doi:10.1109/iccc51575.2020.9345142

Cited by 11 publications

(5 citation statements)

References 9 publications

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“…However, SNs are not considered. Yang et al [32] propose a controllable and obstacle avoidance CUAV routing algorithm to minimize the number of dead SNs in the network and maximize the energy efficiency during charging process. However, the problem of repeated charging is not considered.…”

Section: Related Workmentioning

confidence: 99%

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Liu

Pan

Sun

et al. 2022

IEEE Internet Things J.

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Wireless rechargeable sensor networks with a charging unmanned aerial vehicle (CUAV) have the broad application prospects in the power supply of the rechargeable sensor nodes (SNs). However, how to schedule a CUAV and design the trajectory to improve the charging efficiency of the entire system is still a vital problem. In this paper, we formulate a joint-CUAV scheduling and trajectory optimization problem (JSTOP) to simultaneously minimize the hovering points of CUAV, the number of the repeatedly covered SNs and the flying distance of CUAV for charging all SNs. Due to the complexity of JSTOP, it is decomposed into two optimization subproblems that are CUAV scheduling optimization problem (CSOP) and CUAV trajectory optimization problem (CTOP). CSOP is a hybrid optimization problem that consists of the continuous and discrete solution space, and the solution dimension in CSOP is not fixed since it should be changed with the number of hovering points of CUAV. Moreover, CTOP is a completely discrete optimization problem. Thus, we propose a particle swarm optimization (PSO) with a flexible dimension mechanism, a K-means operator and a punishment-compensation mechanism (PSOFKP) and a PSO with a discretization factor, a 2-opt operator and a path crossover reduction mechanism (PSOD2P) to solve the converted CSOP and CTOP, respectively. Simulation results evaluate the benefits of PSOFKP and PSOD2P under different scales and settings of the network, and the stability of the proposed algorithms is verified.

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Section: Related Workmentioning

confidence: 99%

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Liu

Pan

Sun

et al. 2022

IEEE Internet Things J.

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“…While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges when applying WCVs for wireless charging: off-road navigation and vehicle speed limitations [6]. In order to improve the mentioned shortcomings, recent studies [6][7][8][9][10] have explored the use of unmanned aerial vehicles (UAVs) for charging sensors in WRSNs. UAVs have now found extensive applications in the commercial sector, including surveillance, remote sensing, aerial photography, search and rescue, and on-demand emergency communication.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al [9] proposed a method to charge WRSN sensor nodes using drones, considering energy consumption from flight distance and striving to improve charging efficiency. Yang et al [10] investigated drones equipped with large-capacity batteries to deliver energy to sensor nodes.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

Cheng,

Yu,

Zhang

2024

Algorithms

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The recent advancement in wireless power transmission (WPT) has led to the development of wireless rechargeable sensor networks (WRSNs), since this technology provides a means to replenish sensor nodes wirelessly, offering a solution to the energy challenges faced by WSNs. Most of the recent previous work has focused on charging sensor nodes using wireless charging vehicles (WCVs) equipped with high-capacity batteries and WPT devices. In these schemes, a vehicle can move close to a sensor node and wirelessly charge it without physical contact. While these schemes can mitigate the energy problem to some extent, they overlook two primary challenges of applied WCVs: off-road navigation and vehicle speed limitations. To overcome these challenges, previous work proposed a new WRSN model equipped with one drone coupled with several pads deployed to charge the drone when it cannot reach the subsequent stop. This wireless charging pad deployment aims to deploy the minimum number of pads so that at least one feasible routing path from the base station can be established for the drone to reach every SN in a given WRSN. The major weakness of previous studies is that they only consider deploying a wireless charging pad at the locations of the wireless sensor nodes. Their schemes are limited and constrained because usually every point in the deployed area can be considered to deploy a pad. Moreover, the deployed pads suggested by these schemes may not be able to meet the connected requirements due to sparse environments. In this work, we introduce a new scheme that utilizes the Quad-Tree concept to address the wireless charging pad deployment problem and reduce the number of deployed pads at the same time. Extensive simulations were conducted to illustrate the merits of the proposed schemes by comparing them with different previous schemes on maps of varying sizes. In the case of large maps, the proposed schemes surpassed all previous works, indicating that our approach is more suitable for large-scale network environments.

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“…In the extant research on UAV-Powered WSNs, the primary optimization objectives comprise the maximization of the energy harvested by the SNs and the enhancement of the energy efficiency of the UAVs [5][6][7]. To achieve this goal, designing the trajectory of the UAVs is the key solution.…”

Section: Introductionmentioning

confidence: 99%

Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method

Yang,

et al. 2023

Second International Conference on Electronic Information Technology (EIT 2023)

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Unmanned Aerial Vehicle (UAV)-powered Wireless Sensor Networks (WSNs) are considered to be a promising solution to the problem of the limited power of the Sensor Nodes (SNs). In this paper, we introduce a UAV-powered WSN system, where multi-UAVs undertake the role of remote charging stations. To optimize the overall power efficiency, we design the collaborative trajectories of the UAVs. In order to solve the problem of trajectory planning, we first model the service process as a Markov decision process (MDP), and then propose a Multi-Agent Deep Reinforcement Learning (MADRL) based algorithm named Modified Multi-agent Deep Deterministic Policy Gradient (M2DDPG), which is learned centrally and executed discretely. The simulation has demonstrated the validity, efficacy, and superior performance of the proposed M2DDPG algorithm compared to the baseline algorithm.

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An Efficient Charging Algorithm for UAV-aided Wireless Sensor Networks

Cited by 11 publications

References 9 publications

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Joint Scheduling and Trajectory Optimization of Charging UAV in Wireless Rechargeable Sensor Networks

Optimizing Charging Pad Deployment by Applying a Quad-Tree Scheme

Unmanned aerial vehicle trajectory design in wireless sensor networks: a deep reinforcement learning method

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