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An efficient approximate algorithm for achieving (k − !) barrier coverage in camera wireless sensor networks
Author(s): Nguyen Thi My Binh; Huynh Thi Thanh Binh; Vu Le Loi; Vu Trung Nghia; Dang Lam San; Chu Minh Thang
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Paper Abstract

Barrier coverage problems in wireless camera sensor networks (WCSNs) have drawn the attention by academic community because of their huge potential applications. Various versions of barrier coverage under WCSNs have been studied such as minimal exposure path, strong/weak barrier, 1/k barrier, full view barrier problems. In this paper, based on new (k−ω) coverage model, we study how to achieve (k −ω) barrier coverage problem under uniform random deployment scheme (hereinafter A(k − ω)BC problem). This problem aims to juggle whether any given camera sensor networks is (k − ω) barrier coverage. A camera sensor network is called (k − ω) barrier coverage if any crossing path is (k − ω) coverage. The A(k − ω)BC problem is useful because it can make balance of the number of camera sensors used and the information retrieved by the camera sensors. Furthermore, this problem is vital for design and applications for camera sensor networks when camera sensor nodes were deployed randomly. Thus, we formulate the A(k − ω)BC problem and then proposed an efficient method named Dynamic Partition for solving this problem . An extensive experiments were conducted on random instances, and the results indicated that the proposed algorithm can achieve high quality and stable solutions in real-time execution.

Paper Details

Date Published: 10 May 2019
PDF: 13 pages
Proc. SPIE 11006, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications, 1100613 (10 May 2019); doi: 10.1117/12.2519272
Show Author Affiliations
Nguyen Thi My Binh, Hanoi Univ. of Science and Technology (Viet Nam)
Huynh Thi Thanh Binh, Hanoi Univ. of Science and Technology (Viet Nam)
Vu Le Loi, Hanoi Univ. of Science and Technology (Viet Nam)
Vu Trung Nghia, Hanoi Univ. of Science and Technology (Viet Nam)
Dang Lam San, Hanoi Univ. of Science and Technology (Viet Nam)
Chu Minh Thang, Hanoi Univ. of Science and Technology (Viet Nam)


Published in SPIE Proceedings Vol. 11006:
Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications
Tien Pham, Editor(s)

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