Optimizing the Sensor Movement for Barrier Coverage in a Sink-Based Deployed Mobile Sensor Network

Li, Shuangjuan; Shen, Hong; Huang, Qiong; Guo, Longkun

doi:10.1109/access.2019.2949025

Cited by 4 publications

(3 citation statements)

References 26 publications

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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 proposed algorithm can reduce the communication energy consumption and the moving distance of sensors. Reference [19] pointed out that by deploying the sink stations in a mobile sensor network, the number of sensors required to construct a barrier can be known, and the moving distance of sensors can be optimized. The above-mentioned barrier coverage algorithms form the open barrier which cannot detect intrusion from any direction.…”

Section: Related Workmentioning

confidence: 99%

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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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Section: Related Workmentioning

confidence: 99%

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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“…Green Internet of Things (IoT) typically consists of a large number of low-cost and small-size wireless sensors [1], [2]. Traditionally, sensors measure and monitor ambient conditions in the surrounding environment and then send the measurements and monitored information to a static sink [3]. However, due to the limited capacity of sensors, maintaining sustainable energy supply of sensors becomes a major research challenge [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Age-Optimal Mobile Elements Scheduling for Recharging and Data Collection in Green IoT

Shi

et al. 2020

IEEE Access

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Ensuring real-time reporting of fresh information and maintaining the sustainability of power supply is of great importance in time-critical green Internet of Things (IoT). In this paper, we investigate the mobile element scheduling problem in a network with multiple independent and rechargeable sensors, in which mobile elements are dispatched to collect data packets from the sensor nodes and to recharge them. The age of information (AoI) is used to measure the time elapsed of the most recently delivered packet since the generation of the packet. We propose an age-optimal mobile elements scheduling (AMES), which decides the trajectories of mobile elements based on a cooperative enforcement game and completes the time-slot allocation in each meeting point, to minimize the average AoI and maximize the energy efficiency. The cooperative enforcement game enables the mobile elements to make optimal visiting decisions and avoid the visiting conflicts, and the outcome of the game is pareto-optimal. Compared to the existing approaches, i.e., greedy algorithm (GA), greedy-neighborhood algorithm (GA-neighborhood), simulation results demonstrate that AMES can achieve a lower average AoI and a higher energy efficiency with a higher successful visiting ratio of the sensor node. INDEX TERMS Mobile element scheduling, data collection, age of information, energy efficiency, green IoT.

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Efficient Data Collection in Wireless Sensor Network

Vasani¹,

Maurya²

2021

Computer Networks and Inventive Communication Technologies

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Optimizing the Sensor Movement for Barrier Coverage in a Sink-Based Deployed Mobile Sensor Network

Cited by 4 publications

References 26 publications

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

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

Age-Optimal Mobile Elements Scheduling for Recharging and Data Collection in Green IoT

Efficient Data Collection in Wireless Sensor Network

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