Contribution to the Maximum Coverage Detection in a Heterogeneous Network

Contribution to the Maximum Coverage Detection in a Heterogeneous Network

Hocine Chebi (Faculty of Electrical Engineering, Djillali Liabes University, Sidi Bel Abbes, Algeria), Abdelkader Benaissa (Faculty of Electrical Engineering, Djillali Liabes University, Sidi Bel Abbes, Algeria) and Rafik Sayah (Faculty of Electrical Engineering, Djillali Liabes University, Sidi Bel Abbes, Algeria)
Copyright: © 2020 |Pages: 19
DOI: 10.4018/IJAEC.2020070101

Abstract

This article has addressed the problem of area coverage in surveillance camera networks using a minimum number of active sensors. The dense and random deployment of cameras creates many problems, among which the same portion of the area of interest is cited and monitored by several sensors. This redundancy of information generates unnecessary energy consumption, which increases the cost of installation. This work contributed to the extension of a surveillance algorithm, and the authors presented in this work a distributed algorithm of perimeter surveillance and made this contribution allowing the maintenance of total coverage in heterogeneous camera networks. The proposed solution is based on the search for minimum sets that completely cover a surface by scheduling the activity of the sensors. The proposed approach consists of calculating the distance between the center and the furthest point not covered and subtracting a fixed step from it; the coverage of these circles is done in the same way as the coverage of the first perimeter. The results of the simulations show that this approach ensures maximum coverage with a minimum number of cameras.
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Background On Coverage Algorithms

Monitoring is found in several applications namely, emergency response, loss recovery, traffic monitoring and smart homes (Shirmohammadi 2014). The determination of the confidence of a camera network is an essential object to provide pure information for a series of concentrations such as probe prediction, choice, calibration and optimal placement (Rehder 2017; Liu 2014; Wang 2008). The strong placement of cameras has an astonishing impact on the overall performance and cost of a surveillance system (Kritter 2019). Camera linearization confidence for a specific surveillance area is a challenging task as it involves repetitive changes to camera placement in terms of location and position, while keeping the total number of cameras- photo to a minimum (Mavrinac 2012).

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