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Exploiting Spatio-temporal Correlations for Data Processing in Sensor Networks

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GeoSensor Networks (GSN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4540))

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Abstract

Recent advances in microelectronics have made feasible the deployment of sensor networks for a variety of monitoring and surveillance tasks. In such tasks the state of the network is evaluated either at regular intervals at a base-station, which constitutes a centralized location where the data collected by the sensor nodes can be collected and processed, or continuously through the use of, potentially multiple, continuous queries. In order to increase the network lifetime, multiple techniques have been proposed in order to reduce the data transmitted in the network, since the data communication often constitutes the main source of energy drain in sensor networks. In this work we discuss several data reduction techniques that can be applied for energy-efficient query processing in sensor network applications. All of our proposed techniques seek to identify and take into account the characteristics of the collected data. Depending on the nature of the monitoring application at hand, the targeted data characteristics may range from simply monitoring the variance of a node’s measurements to identifying spatio-temporal correlations amongst the values collected by the sensor nodes.

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Authors and Affiliations

  1. University of Athens, Greece

    Antonios Deligiannakis

  2. Athens University of Economics and Business, Greece

    Yannis Kotidis

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  1. Antonios Deligiannakis
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  2. Yannis Kotidis
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Silvia Nittel Alexandros Labrinidis Anthony Stefanidis

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Deligiannakis, A., Kotidis, Y. (2008). Exploiting Spatio-temporal Correlations for Data Processing in Sensor Networks. In: Nittel, S., Labrinidis, A., Stefanidis, A. (eds) GeoSensor Networks. GSN 2006. Lecture Notes in Computer Science, vol 4540. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79996-2_4

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  • DOI: https://doi.org/10.1007/978-3-540-79996-2_4

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  • Print ISBN: 978-3-540-79995-5

  • Online ISBN: 978-3-540-79996-2

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