JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs

doi:10.3390/s22114120

機構典藏 > College of Engineering > Department of Artificial Intelligence > Journal Article > Item 987654321/124050

Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124050

Title:	JSQE: Joint Surveillance Quality and Energy Conservation for Barrier Coverage in WSNs
Authors:	Shao, Xuemei;Chang, Chih-Yung;Zhao, Shenghui;Kuo, Chin-Hwa;Roy, Diptendu Sinha;Pi, Xinzhe;Yang, Shin-Jer
Keywords:	wireless sensor networks;barrier coverage;ESM;surveillance quality
Date:	2022-05-29
Issue Date:	2023-05-11 12:05:31 (UTC+8)
Publisher:	MDPI AG
Abstract:	Barrier coverage is a fundamental issue in wireless sensor networks (WSNs). Most existing works have developed centralized algorithms and applied the Boolean Sensing Model (BSM). However, the critical characteristics of sensors and environmental conditions have been neglected, which leads to the problem that the developed mechanisms are not practical, and their performance shows a large difference in real applications. On the other hand, the centralized algorithms also lack scalability and flexibility when the topologies of WSNs are dynamically changed. Based on the Elfes Sensing Model (ESM), this paper proposes a distributed Joint Surveillance Quality and Energy Conservation mechanism (JSQE), which aims to satisfy the requirements of the desired surveillance quality and minimize the number of working sensors. The proposed JSQE first evaluates the sensing probability of each sensor and identifies the location of the weakest surveillance quality. Then, the JSQE further schedules the sensor with the maximum contribution to the bottleneck location to improve the overall surveillance quality. Extensive experiment results show that our proposed JSQE outperforms the existing studies in terms of surveillance quality, the number of working sensors, and the efficiency and fairness of surveillance quality. In particular, the JSQE improves the surveillance quality by 15% and reduces the number of awake sensors by 22% compared with the relevant TOBA.
Relation:	Sensors 22(11), 4102
DOI:	10.3390/s22114120
Appears in Collections:	[Department of Artificial Intelligence] Journal Article

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