MCDP: Maximizing Cooperative Detection Probability for Barrier Coverage in Rechargeable Wireless Sensor Networks

Xu, Pei; Wu, Jian; Chang, Chih‐Yung; Shang, Cuijuan; Roy, Diptendu Sinha

doi:10.1109/jsen.2020.3043456

Cited by 10 publications

(6 citation statements)

References 25 publications

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“…In this model, the probability value of the PoIs being monitored by the sensor decreases with the growth of the distance between them. The perception model is as follows [34]:…”

Section: Sensor Perception Modelmentioning

confidence: 99%

“…Extensive simulations are performed to verify the accuracy, scalability and efficiency of the proposed algorithm. In these simulations, the PoIs are randomly distributed in a two-dimensional space 𝛷 of 50 m × 50 m. The parameters used in the simulations are shown in Table 3, where the parameters in the sensing model and charging model refer to those in [34,35], respectively.…”

Section: Simulation Setupmentioning

confidence: 99%

See 1 more Smart Citation

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Lian,

Yao

2024

Energies

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As a promising technology to achieve the permanent operation of battery-powered wireless sensor devices, wireless rechargeable sensor networks (WRSNs) by radio-frequency radiation have attracted considerable attention in recent years. Determining how to save the deployment cost of WRSNs has been a hot topic. Previous scholars have mainly studied the cost of deploying chargers, thus ignoring the impact of sensor deployment on the network. Therefore, we consider the new problem of joint deployment of sensors and chargers on a two-dimensional plane, i.e., deploying the minimum number of sensors and chargers used to monitor points of interest (PoIs). Considering the interaction of deployed sensors and chargers, we divide the problem into two stages, P1 and P2. P1 addresses the sensor deployment, while P2 addresses the deployment of chargers. Both P1 and P2 have proved to be NP-hard. Meanwhile, we notice that the aggregation effect of sensors can effectively reduce the number of chargers deployed; therefore, we propose a greedy heuristic approximate solution for deploying sensors by using the aggregation effect (GHDSAE). Then, a greedy heuristic (GH) solution and a particle swarm optimization (PSO) solution are proposed for P2. The time complexity of these solutions is analyzed. Finally, extensive simulation results show that the PSO solution can always reduce the number of chargers deployed based on the GHDSAE solution sensor deployment approach. Therefore, it is more cost-effective to jointly deploy sensors and chargers by using the GHDSAE solution and the PSO solution.

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“…In this model, the probability value of the PoIs being monitored by the sensor decreases with the growth of the distance between them. The perception model is as follows [34]:…”

Section: Sensor Perception Modelmentioning

confidence: 99%

Section: Simulation Setupmentioning

confidence: 99%

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Lian,

Yao

2024

Energies

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“…Open barrier coverage [11][12][13][14][15][16][17][18][19]: The authors of [11] pointed out the limitations of the barrier coverage algorithms based on global information and developed a barrier coverage protocol based on local information (LBCP). LBCP can not only guarantee local barrier coverage but also prolong the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

“…The number of nodes required to construct a k-barrier is reduced by developing the cover adjacent net, and a barrier energy scheduling is proposed to achieve its energy balance. Reference [16] considered the barrier coverage problem in rechargeable sensor networks based on the probability sensing model and proposed a barrier coverage algorithm called MCDP. The MCDP calculates the detection probability of each sensor to each space time point and schedules sensors to stay in the sensing state and charging state in each time slot.…”

Section: Related Workmentioning

confidence: 99%

UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning

Li¹,

Chen²

2022

Sensors

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Target-barrier coverage is a newly proposed coverage problem in wireless sensor networks (WSNs). The target-barrier is a closed barrier with a distance constraint from the target, which can detect intrusions from outside. In some applications, detecting intrusions from outside and monitoring the targets inside the barrier is necessary. However, due to the distance constraint, the target-barrier fails to monitor and detect the target breaching from inside in a timely manner. In this paper, we propose a convex hull attraction (CHA) algorithm to construct the target-barrier and a UAV-enhanced coverage (QUEC) algorithm based on reinforcement learning to cover targets. The CHA algorithm first divides the targets into clusters, then constructs the target-barrier for the outermost targets of the clusters, and the redundant sensors replace the failed sensors. Finally, the UAV’s path is planned based on QUEC. The UAV always covers the target, which is most likely to breach. The simulation results show that, compared with the target-barrier construction algorithm (TBC) and the virtual force algorithm (VFA), CHA can reduce the number of sensors required to construct the target-barrier and extend the target-barrier lifetime. Compared with the traveling salesman problem (TSP), QUEC can reduce the UAV’s coverage completion time, improve the energy efficiency of UAV and the efficiency of detecting targets breaching from inside.

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“…In practical network applications, the quality of information (QoI) is essential in network performance. Different performance indexes are used to evaluate network QoI for different applications, including quality of coverage [16,17] in environmental monitoring networks, tracking accuracy [8] in target tracking networks, and detection probability [18,19] in event detection networks. The energy consumption of nodes in the network is different.…”

Section: Introductionmentioning

confidence: 99%

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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MCDP: Maximizing Cooperative Detection Probability for Barrier Coverage in Rechargeable Wireless Sensor Networks

Cited by 10 publications

References 25 publications

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

Joint Deployment of Sensors and Chargers in Wireless Rechargeable Sensor Networks

UAV Enhanced Target-Barrier Coverage Algorithm for Wireless Sensor Networks Based on Reinforcement Learning

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

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