A data sample algorithm applied to wireless sensor network with disruptive connections

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 146; pp. 1 - 11
• Main Authors: Vasconcelos, Israel L.C., Martins, Ivan C., Figueiredo, Carlos M.S., Aquino, Andre L.L.
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 09.12.2018; Elsevier Sequoia S.A
• Subjects: Algorithms; Application modeling; Covariance; Data acquisition; Data sample algorithms; Kriging interpolation; Random walk; Remote sensing; Remote sensors; Sensors; Software; Strategy; Wildlife observation; Wireless networks; Wireless sensor networks; Data sample algorithms; Wireless sensor networks; Application modeling
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2018.09.006

This paper presents a data sample algorithm applied to wireless sensor network applications with disruptive connections. Additionally, it defines a model for delay-tolerant sensor network where drop strategies are applied to improve the phenomenon coverage in an application that monitors the forest temperature incidence for wildlife observation. The environmental application model comprises: i) Phenomenon generation based on a Gaussian random field along with the Matern covariance model; ii) Sensing nodes deployment based on simple sequential inhibition process with a mobile sink node following a random walk process; iii) Data collection and processing based on a data-aware drop strategy; and iv) Phenomenon reconstruction based on simple kriging interpolation. This research employed the data-aware drop strategy and compared it with the others, reported in the literature. Besides the satisfactory application of this model, the results show that the performance of data-aware drop strategy is twice better than conventional ones in all evaluated scenarios.