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Gradual Event Detection in Sensor Networks

Author: PengLiPing
Tutor: GaoHong
School: Harbin Institute of Technology
Course: Computer Science and Technology
Keywords: Wireless Sensor Networks Event Detection Gradual Event
CLC: TP212.9
Type: Master's thesis
Year: 2008
Downloads: 52
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Abstract


Wireless sensor networks (WSN) are of great significance in resolving many real-world problems, and have attracted increasing research interests in recent years. One of the most important applications of WSN is the detection of events, most of which in the real-world have the property of gradualness– we call them gradual events, such as fire in the forest and gas leakage in the coal mine. The range of a gradual event changes slowly and the effect of a gradual event attenuates gradually as the distance from the event increases.Event is commonly defined by thresholds on several attributes. Event detection can be divided into two phases: event reporting and event boundary tracking. It requires the network to report to the sink node as soon as possible if an event occurs in the surveillance area, and also requires the network not to falsely report if no event actually occurs. Since the energy is the bottleneck of sensor networks, another goal of event detection is to be energy-efficient.In the phase of event reporting, the sink needs to know that whether there is any event in the surveillance area covered by the network. Due to the fact that readings of a failed sensor node do not represent the true attribute values, and considering that node failures are independent but the effect on sensor readings brought by events is spatial relevant, in order to disambiguate node failures from events, previous literatures suggested a mechanism which makes neighbor nodes vote according to whether their readings reach the threshold, instead of reporting an event directly based on the judgement of single sensor node. Although such mechanism significantly reduces false positives, it inevitably introduces false negatives which lead to a detection delay under the scenario of gradual events. In this paper, we propose a new detection method– the bit-string match voting (BMV), which provides a response time close to that of the direct reporting method and a false positive rate even lower than that of the threshold-based voting method. Furthermore, a further optimization on BMV is able to avoid repeated and redundant reports of the same event, thus prolongs the life of the network.In the phase of event boundary tracking, the sink needs to know the location and the size of an event after it occurs. We expect a tracking method that is able to energy-efficiently track current event boundary of any shape and also ensures the effective detection of new events. Previous method of event boundary tracking just periodically repeats the work of recognizing the event boundary, which will unnecessarily waste much energy. In this paper, we introduce a difference-based tracking method,which will surely not lose the boundary of current events as well as not miss the boundary of new event. When tracking gradual events, this method can significantly reduce the number of sensor nodes that need to communicate with others, thus saves the energy and prolongs the life of the network.Extensive simulations and theoretical analysis verify that the methods proposed in this paper can fast, reliably and energy-efficiently detect gradual events.

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CLC: > Industrial Technology > Automation technology,computer technology > Automation technology and equipment > Automation components,parts > Transmitter ( converter),the sensor > Sensor applications
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