FoV-Based Quality Assessment and Optimization for Area Coverage in Wireless Visual Sensor Networks

• Type: Article in International Scientific Journal
• Date: 2020

Authors

Thiago C. Jesus (Author) Other; Daniel G. Costa (Author) Other; Paulo Portugal (Author) FEUP; Francisco Vasques (Author) FEUP

Journal

Title: IEEE Access

Vol. 8

Pages: 109568 -109580

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 4 Citations

Scopus - 4 Citations

Other information

Authenticus ID: P-00S-CQC

DOI: 10.1109/access.2020.3002206

Abstract (EN): Wireless visual sensor networks are commonly employed on several applications contexts such as smart cities, intelligent transportation systems and industrial management, aiming at the use of visual data from cameras to provide enhanced information and to expand the networks utilities. In these scenarios, some applications may require high-definition images when performing more specialized tasks, for example in face and text recognition, adding an important monitoring requirement when using camera-based sensors. In fact, it is important to ensure that the network is able to gather visual data with the associated required quality to each task, and such perceived quality may be processed as a function of the Field of View (FoV) of the visual sensors. In order to address this issue, new quality metrics are proposed for wireless visual sensor networks that are deployed to perform area coverage, exploiting for that different perceptions of the FoV. Those metrics are proposed along with redeployment optimization methods for visual sensor nodes aiming at the improvement of the perceived monitoring quality, which are based on greedy and evolutionary-based approaches. The proposed metrics and algorithms are expected to be more realistic than previous solutions, allowing flexible processing of variables as cameras & x2019; positions, orientations and viewing angles, providing then high flexibility on the definition of parameters and significantly contributing to the development of sensor networks based on visual sensors.

Language: English

Type (Professor's evaluation): Scientific

Notes: https://www.authenticus.pt/P-00S-DKQ

No. of pages: 13