Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks

Li, Jinglin; Jiang, Chengpeng; Wang, Jing; Xu, Taian; Xiao, Wendong

doi:10.3390/app13052840

Open AccessArticle

Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks

by

Jinglin Li

^1,2,

Chengpeng Jiang

^1,2,

Jing Wang

³

,

Taian Xu

^4,* and

Wendong Xiao

^1,2,3,*

¹

School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China

²

Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China

³

Shunde Innovation School, University of Science and Technology Beijing, Shunde 528399, China

⁴

Zaozhuang University, Zaozhuang 277160, China

^*

Authors to whom correspondence should be addressed.

Appl. Sci. 2023, 13(5), 2840; https://doi.org/10.3390/app13052840

Submission received: 11 January 2023 / Revised: 10 February 2023 / Accepted: 16 February 2023 / Published: 22 February 2023

(This article belongs to the Special Issue Practical Applications of New Optimization Methods and Intelligent Control)

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Abstract

In wireless rechargeable sensor networks (WRSNs), a novel approach to energy replenishment is offered by the utilization of mobile chargers (MCs), which charge nodes via wireless energy transfer technology. However, previous research on mobile charging schemes has commonly prioritized charging efficiency as a performance index, neglecting the importance of quality of sensing coverage (QSC). As the network scale increases, the MC’s charging power becomes unable to meet the energy needs of all nodes, leading to a decline in network QSC when nodes’ energy is depleted. To solve this problem, we study the problem of mobile charging sequence scheduling for optimal network QSC (MSSQ) and propose an improved quantum-behaved particle swarm optimization (IQPSO) algorithm. With the attraction of potential energy in quantum space, this algorithm will adaptively adjust the contraction expansion coefficient iteratively, leading to a global optimal solution for the mobile charging sequence. Extensive simulation results demonstrate the superiority of IQPSO over the widely used QPSO and Greedy algorithms in terms of network QSC, especially in large-scale networks.

Keywords: wireless rechargeable sensor networks; quality of sensing coverage; mobile charging sequence scheduling; contraction expansion coefficient; improved quantum-behaved particle swarm optimization

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MDPI and ACS Style

Li, J.; Jiang, C.; Wang, J.; Xu, T.; Xiao, W. Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks. Appl. Sci. 2023, 13, 2840. https://doi.org/10.3390/app13052840

AMA Style

Li J, Jiang C, Wang J, Xu T, Xiao W. Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks. Applied Sciences. 2023; 13(5):2840. https://doi.org/10.3390/app13052840

Chicago/Turabian Style

Li, Jinglin, Chengpeng Jiang, Jing Wang, Taian Xu, and Wendong Xiao. 2023. "Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks" Applied Sciences 13, no. 5: 2840. https://doi.org/10.3390/app13052840

APA Style

Li, J., Jiang, C., Wang, J., Xu, T., & Xiao, W. (2023). Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks. Applied Sciences, 13(5), 2840. https://doi.org/10.3390/app13052840

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks

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