On redundant coverage maximization in wireless visual sensor networks: Evolutionary algorithms for multi-objective optimization

• Type: Article in International Scientific Journal
• Date: 2019

Authors

Rangel, EO (Author) Other; Costa, DG (Author) Other; Loula, A (Author) Other

Journal

Title: Applied Soft Computing Journal

Vol. 82

ISSN: 1568-4946

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 19 Citations

Scopus - 18 Citations

Other information

Authenticus ID: P-00W-KRX

DOI: 10.1016/j.asoc.2019.105578

Abstract (EN): Wireless visual sensor networks can provide valuable information for a variety of monitoring and control applications. Frequently, a set of targets must be covered by visual sensors, as such visual sensing redundancy is a desired condition specially when applications have availability requirements for multiple coverage perspectives. If visual sensors become rotatable, their sensing orientations can be adjusted to optimize coverage and redundancy, bringing different challenges as there may be different coverage optimization objectives. Actually, the specific issue of redundant coverage maximization is inherently a multi-objective problem, but usual approaches are not designed accordingly to compute visual sensing redundancy. This article proposes two different evolutionary algorithms that exploit the multi-objective nature of the redundant coverage maximization problem: a lexicographic ¿a priori¿ algorithm and a NSGA-II ¿a posteriori¿ algorithm. The performance of both algorithms are compared, using a previously proposed single-objective greedy-based algorithm as a reference. Numerical results outline the benefits of employing evolutionary algorithms for adjustments of sensors¿ orientations, potentially benefiting deployment and management of wireless visual sensor networks for different monitoring scenarios.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14