Non-linear K-Barrier Coverage in Mobile Sensor Network

Ma, Zijing; Li, Shuangjuan; Guo, Longkun; Wang, Guohua

doi:10.1007/978-981-15-2767-8_2

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Most strategies reduce network energy consumption by scheduling nodes to ensure network QoI under limited network energy [20]; research on QoI optimization in WSNs is limited. Moreover, Gaudette et al [7] optimized and controlled each node's sensing range to maximize the network QSC.…”

Section: Network Qoi Optimizationmentioning

confidence: 99%

“…It leads to the vulnerabilities in network sensing coverage, information loss, and reduced reliability. Although the optimization of QoI in WSNs has become more sophisticated [20], more research needs to be conducted on WRSNs. As a critical factor in evaluating the network coverage performance, quality of sensing coverage (QSC) is the foundation of network credibility.…”

Section: Introductionmentioning

confidence: 99%

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

Jiang

Wang

et al. 2023

Applied Sciences

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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.

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Section: Network Qoi Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%