Sensors 2010, 10(12), 11001-11020; doi:10.3390/s101211001
Article

On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition

1 Department of Computer Science, University of Cape Town, 7707 Cape Town, South Africa 2 CSIR Modelling & Digital Sciences, Building 17A, Pretoria, South Africa 3 The Abdus Salam International Centre for Theretical Physics, Trieste, Italy
* Author to whom correspondence should be addressed.
Received: 8 October 2010; in revised form: 1 November 2010 / Accepted: 22 November 2010 / Published: 3 December 2010
(This article belongs to the Special Issue Intelligent Sensors - 2010)
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Abstract: Achieving situation recognition in ubiquitous sensor networks (USNs) is an important issue that has been poorly addressed by both the research and practitioner communities. This paper describes some steps taken to address this issue by effecting USN middleware intelligence using an emerging situation awareness (ESA) technology. We propose a situation recognition framework where temporal probabilistic reasoning is used to derive and emerge situation awareness in ubiquitous sensor networks. Using data collected from an outdoor environment monitoring in the city of Cape Town, we illustrate the use of the ESA technology in terms of sensor system operating conditions and environmental situation recognition.
Keywords: wireless sensor networks; energy efficiency; situation awareness; situation recognition; probabilistic model

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MDPI and ACS Style

Bagula, A.B.; Osunmakinde, I.; Zennaro, M. On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition. Sensors 2010, 10, 11001-11020.

AMA Style

Bagula AB, Osunmakinde I, Zennaro M. On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition. Sensors. 2010; 10(12):11001-11020.

Chicago/Turabian Style

Bagula, Antoine B.; Osunmakinde, Isaac; Zennaro, Marco. 2010. "On the Relevance of Using Bayesian Belief Networks in Wireless Sensor Networks Situation Recognition." Sensors 10, no. 12: 11001-11020.

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