Trajectory Optimization for UAVs’ Efficient Charging in Wireless Rechargeable Sensor Networks

Wu, Pengfei; Xiao, Fu; Sha, Chao; Huang, Haiping; Sun, Lijuan

doi:10.1109/tvt.2020.2969220

Cited by 66 publications

(41 citation statements)

References 39 publications

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“…Wu et al studied the UAV's trajectory optimization from the viewpoint of UAV's energy utilization efficiency. For this purpose, they presented a polynomial-time randomized approximation scheme (PARS) to obtain the minimal number of hovering locations [29]. Subsequently, to achieve balanced energy consumption among UAVs, they maximized the energy utilization efficiency of UAVs, and they minimized the communication delay by optimizing the trajectory jointly with constraints of the energy capacity and the area of the target region [30].…”

Section: Related Workmentioning

confidence: 99%

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Qin

Zhao

et al. 2021

Remote Sensing

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Power consumption in wireless sensor networks is high, and the lifetime of a battery has become a bottleneck, restricting network performance. Wireless power transfer with a ground mobile charger is vulnerable to interference from the terrain and other factors, and hence it is difficult to deploy in practice. Accordingly, a novel paradigm is adopted where a multi-UAV (unmanned aerial vehicle) with batteries can transfer power and information to SDs (sensor devices) in a large-scale sensor network. However, there are discrete events, continuous process, time delay, and decisions in such a complicated system. From the perspective of a hybrid system, a hybrid colored cyber Petri net system is proposed here to depict and analyze this problem. Furthermore, the energy utilization rate and information collection time delay are conflict with each other; therefore, UAV-aided wireless power and information transfer is formulated as a multi-objective optimization problem. For this reason, the MAC-NSGA II (multiple ant colony-nondominated sorting genetic algorithm II) is proposed in this work. Firstly, the optimal trajectory of multiple UAVs was obtained, and on this basis, the above two objectives were optimized simultaneously. Large-scale simulation results show that the proposed algorithm is superior to NSGA II and MOEA/D in terms of energy efficiency and information collection delay.

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Section: Related Workmentioning

confidence: 99%

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Qin

Zhao

et al. 2021

Remote Sensing

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“…However, most of the previous studies are based on complex optimization solutions, evolutionary algorithms, or heuristic approaches [26]- [29]. Nowadays, the new research tendency of using artificial intelligence (AI), precisely, Reinforcement Learning (RL) came out as a new strategy that can grant the flying units sufficient intelligence to make local decisions to accomplish missions.…”

Section: A Literature Reviewmentioning

confidence: 99%

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

et al. 2020

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Nowadays, Wireless Sensor Networks (WSNs) are playing a vital and sustainable role in many verticals touching different aspects of our lives including civil, public, and military applications. WSNs majorly consist of a few to several sensor nodes, that are connected to each other via wireless communication links and require real-time or delayed data transfer. In this paper, we propose an autonomous Unmanned Aerial Vehicle (UAV)-enabled data gathering mechanism for delay-tolerant WSN applications. The objective is to employ a self-trained UAV as a flying mobile unit collecting data from ground sensor nodes spatially distributed in a given geographical area during a predefined period of time. In this approach, two Reinforcement Learning (RL) approaches, specifically Deep Deterministic Gradient Decent (DDPG) and Q-learning (QL) algorithms, are jointly employed to train the UAV to understand the environment and provide effective scheduling to accomplish its data collection mission. The DDPG is used to autonomously decide the best trajectory to adopt in an obstacle-constrained environment, while the QL is developed to determine the order of nodes to visit such that the data collection time is minimized. The schedule is obtained while considering the limited battery capacity of the flying unit, its need to return the charging station, the time windows of data acquisition, and the priority of certain sensor nodes. Customized reward functions are designed for each RL model and, through numerical simulations, we investigate their training performances. We also analyze the behavior of the autonomous UAV for different selected scenarios and corroborate the ability of the proposed approach in performing effective data collection. A comparison with the deterministic optimal solution is provided to validate the performance of the learning-based approach. INDEX TERMS Internet-of-things, data gathering, reinforcement learning, scheduling, unmanned areal vehicles

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“…Networks which are composed of rechargeable sensor nodes are named as wireless rechargeable sensor networks (WRSNs). Research hotspots on WRSNs mainly focus on solving the problems of both charging scheduling and system performance optimizations [ 7 , 8 , 9 ] in recent years. However, security issues in WRSNs are seldom attracting the attention of scholars.…”

Section: Introductionmentioning

confidence: 99%

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model

Liu

Peng

Zhong

2021

Sensors

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Energy constraint hinders the popularization and development of wireless sensor networks (WSNs). As an emerging technology equipped with rechargeable batteries, wireless rechargeable sensor networks (WRSNs) are being widely accepted and recognized. In this paper, we research the security issues in WRSNs which need to be addressed urgently. After considering the charging process, the activating anti-malware program process, and the launching malicious attack process in the modeling, the susceptible–infected–anti-malware–low-energy–susceptible (SIALS) model is proposed. Through the method of epidemic dynamics, this paper analyzes the local and global stabilities of the SIALS model. Besides, this paper introduces a five-tuple attack–defense game model to further study the dynamic relationship between malware and WRSNs. By introducing a cost function and constructing a Hamiltonian function, the optimal strategies for malware and WRSNs are obtained based on the Pontryagin Maximum Principle. Furthermore, the simulation results show the validation of the proposed theories and reveal the influence of parameters on the infection. In detail, the Forward–Backward Sweep method is applied to solve the issues of convergence of co-state variables at terminal moment.

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Trajectory Optimization for UAVs’ Efficient Charging in Wireless Rechargeable Sensor Networks

Cited by 66 publications

References 39 publications

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

Petri-Net Based Multi-Objective Optimization in Multi-UAV Aided Large-Scale Wireless Power and Information Transfer Networks

A UAV-Assisted Data Collection for Wireless Sensor Networks: Autonomous Navigation and Scheduling

Epidemic Analysis of Wireless Rechargeable Sensor Networks Based on an Attack–Defense Game Model

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