Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.

Energy-Efficient Data Mining Techniques for Emergency Detection in Wireless Sensor Networks

CUZZOCREA, Alfredo Massimiliano
2016

Abstract

Event detection is an important part in many Wireless Sensor Network (WSN) applications such as forest fire, environmental pollution. In this kind of applications, the event must be detected early in order to reduce the threats, damages. In this paper, we propose a new approach for early forest fire detection, which is based on the integration of Data Mining techniques into sensor nodes. The idea is to partition the node set into clusters so that each node can individually detect fires using classification techniques. Once a fire is detected, the corresponding node will send an alert to its cluster-head. This alert will then be routed via gateways, other cluster-heads to the sink in order to inform the firefighters. The approach is validated using the CupCarbon simulator. The results show that our approach can provide a fast reaction to forest fires with efficient energy consumption.
9781509027705
Data Mining
Fire detection
Intelligent Decision Making
Wireless sensor networks
Artificial Intelligence
Computer Networks and Communications
Computer Science Applications1707 Computer Vision and Pattern Recognition
1707
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/20.500.11770/312799
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