Mobile Charging Sequence Scheduling for Optimal Sensing Coverage in Wireless Rechargeable Sensor Networks
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Energy Harvesting in Wireless Networks · Wireless Power Transfer Systems · Innovative Energy Harvesting Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing 100083, China
Beijing Engineering Research Center of Industrial Spectrum Imaging, Beijing 100083, China
Shunde Innovation School, University of Science and Technology Beijing, Shunde 528399, China
Zaozhuang University, Zaozhuang 277160, China
Funding
National Natural Science Foundations of China (NSFC) · Foshan Science and Technology Innovation Special Project · Regional Joint Fund of the Guangdong Basic and Applied Basic Research Fund
References (34)
Show source-provided references
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