Modelling Coverage Failures Caused by Mobile Obstacles for the Selection of Faultless Visual Nodes in Wireless 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; Ana Aguiar (Author) FEUP

Journal

Title: IEEE Access

Vol. 8

Pages: 41537-41550

ISSN: 2169-3536

Publisher: IEEE

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 12 Citations

Other information

Authenticus ID: P-00R-TCP

DOI: 10.1109/access.2020.2977173

Abstract (EN): Wireless sensor networks comprising nodes equipped with cameras have become common in many scenarios, providing valuable visual data for some relevant services such as localization, tracking, patterns identification and emergencies detection. In this context, algorithms and optimization approaches have been designed to perform different types of quality assessment or performance enhancement tasks, addressing challenging issues such as networking, compression, availability, reliability, security, energy efficiency and virtually any subject related to the operational challenges of those networks. However, the dynamics of coverage failures have not been properly modelled in visual sensor networks, resulting in unrealistic perceptions when optimizing or assessing quality in most visual sensing scenarios. Particularly, the Field of View of visual sensors will be affected by occlusion caused by obstacles in the monitored field, which may turn such sensors inadequate for the expected monitoring services of the considered network. Therefore, this article proposes a mathematical model to assess occlusion caused by mobile obstacles such as vehicles on a road or forklifts in an industrial plant, aiming at the selection of the visual sensor nodes that will not have their coverage significantly restricted by those obstacles. Doing so, the proposed model can be exploited by any optimization or quality assessment approach in wireless visual sensor networks, providing a preprocessing method when selecting visual nodes.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 14