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15673 Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks
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Bahrepour, M. and Meratnia, N. and Havinga, P.J.M. (2009) Use of AI Techniques for Residential Fire Detection in Wireless Sensor Networks. In: AIAI 2009 Workshop Proceedings, 23-25 April 2009, Greece. pp. 311-321. ceur-ws.org. ISSN 1613-0073

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Abstract

Early residential fire detection is important for prompt extinguishing and reducing damages and life losses. To detect fire, one or a combination of sensors and a detection algorithm are needed. The sensors might be part of a wireless sensor network (WSN) or work independently. The previous research in the area of fire detection using WSN has paid little or no attention to investigate the optimal set of sensors as well as use of learning mechanisms and Artificial Intelligence (AI) techniques. They have only made some assumptions on what might be considered as appropriate sensor or an arbitrary AI technique has been used. By closing the gap between traditional fire detection techniques and modern wireless sensor network capabilities, in this paper we present a guideline on choosing the most optimal sensor combinations for accurate residential fire detection. Additionally, applicability of a feed forward neural network (FFNN) and Naïve Bayes Classifier is investigated and results in terms of detection rate and computational complexity are analyzed.

Item Type:Conference or Workshop Paper (Full Paper, Talk)
Research Group:EWI-PS: Pervasive Systems
Research Program:CTIT-WiSe: Wireless and Sensor Systems
Research Project:SENSEI: Integrating the PhySical with the Digital World of the Network of the Future, Aware: Platform for Autonomous self-deploying and operation of Wireless sensor-actuator networks cooperating with AeRial objEcts
Uncontrolled Keywords:Neural Networks, Sensor Networks, Event Detection
ID Code:15673
Status:Published
Deposited On:08 September 2009
Refereed:Yes
International:Yes
More Information:statisticsmetis

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