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An On-Line Piecewise Linear Approximation Technique for Wireless Sensor Networks

Berlin, Eugen and Van Laerhoven, Kristof (2010):
An On-Line Piecewise Linear Approximation Technique for Wireless Sensor Networks.
Denver, Colorado, USA, IEEE Computer Society, In: 5th IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2010), [Conference or Workshop Item]

Abstract

Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data. Experiments show that, at the cost of slightly more processing power on the node, our algorithm performs a shape abstraction of the sensed time series which, depending on the nature of the data, can extensively reduce network traffic and nodes’ power consumption.

Item Type: Conference or Workshop Item
Erschienen: 2010
Creators: Berlin, Eugen and Van Laerhoven, Kristof
Title: An On-Line Piecewise Linear Approximation Technique for Wireless Sensor Networks
Language: English
Abstract:

Many sensor network applications observe trends over an area by regularly sampling slow-moving values such as humidity or air pressure (for example in habitat monitoring). Another well-published type of application aims at spotting sporadic events, such as sudden rises in temperature or the presence of methane, which are tackled by detection on the individual nodes. This paper focuses on a zone between these two types of applications, where phenomena that cannot be detected on the nodes need to be observed by relatively long sequences of sensor samples. An algorithm that stems from data mining is proposed that abstracts the raw sensor data on the node into smaller packet sizes, thereby minimizing the network traffic and keeping the essence of the information embedded in the data. Experiments show that, at the cost of slightly more processing power on the node, our algorithm performs a shape abstraction of the sensed time series which, depending on the nature of the data, can extensively reduce network traffic and nodes’ power consumption.

Place of Publication: Denver, Colorado, USA
Publisher: IEEE Computer Society
Divisions: 20 Department of Computer Science > Embedded Sensing Systems
20 Department of Computer Science
Event Title: 5th IEEE International Workshop on Practical Issues in Building Sensor Network Applications (SenseApp 2010)
Date Deposited: 17 Jan 2012 09:46
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