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Sensors 2017, 17(12), 2945;

Pre-Scheduled and Self Organized Sleep-Scheduling Algorithms for Efficient K-Coverage in Wireless Sensor Networks

Department of Computer Science and Information Engineering, Chang Gung University, Guishan, Taoyuan 33302, Taiwan
Division of Cardiology, Department of Internal Medicine, Chang Gung Memorial Hospital, Linkou, Taoyuan 33305, Taiwan
Department of Computer Science and Engineering, Yuan Ze University, Chung-Li District, Taoyuan 32003, Taiwan
Author to whom correspondence should be addressed.
Received: 14 November 2017 / Revised: 8 December 2017 / Accepted: 14 December 2017 / Published: 19 December 2017
(This article belongs to the Section Sensor Networks)
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The K-coverage configuration that guarantees coverage of each location by at least K sensors is highly popular and is extensively used to monitor diversified applications in wireless sensor networks. Long network lifetime and high detection quality are the essentials of such K-covered sleep-scheduling algorithms. However, the existing sleep-scheduling algorithms either cause high cost or cannot preserve the detection quality effectively. In this paper, the Pre-Scheduling-based K-coverage Group Scheduling (PSKGS) and Self-Organized K-coverage Scheduling (SKS) algorithms are proposed to settle the problems in the existing sleep-scheduling algorithms. Simulation results show that our pre-scheduled-based KGS approach enhances the detection quality and network lifetime, whereas the self-organized-based SKS algorithm minimizes the computation and communication cost of the nodes and thereby is energy efficient. Besides, SKS outperforms PSKGS in terms of network lifetime and detection quality as it is self-organized. View Full-Text
Keywords: wireless sensor networks; scheduling; K-coverage; energy efficient wireless sensor networks; scheduling; K-coverage; energy efficient

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Sahoo, P.K.; Thakkar, H.K.; Hwang, I.-S. Pre-Scheduled and Self Organized Sleep-Scheduling Algorithms for Efficient K-Coverage in Wireless Sensor Networks. Sensors 2017, 17, 2945.

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