Overview
- Introduces the trend cluster, a recently defined spatio-temporal pattern, and its use in summarizing, interpolating and identifying anomalies in sensor networks
- Illustrates the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants
- Discusses new possibilities for surveillance enabled by recent developments in sensing technology
Part of the book series: SpringerBriefs in Computer Science (BRIEFSCOMPUTER)
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About this book
Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data. This work introduces a recently defined spatio-temporal pattern, called trend cluster, to summarize, interpolate and identify anomalies in a sensor network. As an example, the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants is discussed. The work closes with remarks on new possibilities for surveillance enabled by recent developments in sensing technology.
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Table of contents (5 chapters)
Authors and Affiliations
Bibliographic Information
Book Title: Data Mining Techniques in Sensor Networks
Book Subtitle: Summarization, Interpolation and Surveillance
Authors: Annalisa Appice, Anna Ciampi, Fabio Fumarola, Donato Malerba
Series Title: SpringerBriefs in Computer Science
DOI: https://doi.org/10.1007/978-1-4471-5454-9
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Author(s) 2014
Softcover ISBN: 978-1-4471-5453-2Published: 27 September 2013
eBook ISBN: 978-1-4471-5454-9Published: 12 September 2013
Series ISSN: 2191-5768
Series E-ISSN: 2191-5776
Edition Number: 1
Number of Pages: XIII, 105
Number of Illustrations: 2 b/w illustrations, 37 illustrations in colour
Topics: Data Mining and Knowledge Discovery, Computer Communication Networks