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Data Mining Techniques in Sensor Networks - Summarization, Interpolation and Surveillance

Englisch · Taschenbuch

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Beschreibung

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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.

Inhaltsverzeichnis

Introduction
Sensor Networks and Data Streams: Basics
Geodata Stream Summarization
Missing Sensor Data Interpolation
Sensor Data Surveillance
Sensor Data Analysis Applications

Zusammenfassung

Emerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through a number of (wireless) remote sensors. Operatively, remote sensors are installed across a spatially distributed network; they gather information along a number of attribute dimensions and periodically feed a central server with the measured data. The server is required to monitor these data, issue possible alarms or compute fast aggregates. As data analysis requests, which are submitted to a server, may concern both present and past data, the server is forced to store the entire stream. But, in the case of massive streams (large networks and/or frequent transmissions), the limited storage capacity of a server may impose to reduce the amount of data stored on the disk.  One solution to address the storage limits is to compute summaries of the data as they arrive and use these summaries to interpolate the real data which are discarded instead.  On any future demands of further analysis of the discarded data, the server pieces together the data from the summaries stored in database and processes them according to the requests.
This work introduces the multiple possibilities and facets of a recently defined spatio-temporal pattern, called trend cluster, and its applications to summarize, interpolate and identify anomalies in a sensor network.   As an example application, the authors illustrate the application of trend cluster discovery to monitor the efficiency of photovoltaic power plants. The work closes with remarks on new possibilities for surveillance gained by recent developments of sensing technology, and with an outline of future challenges.

Produktdetails

Autoren Annalis Appice, Annalisa Appice, Ann Ciampi, Anna Ciampi, Fabio Fumarola, Fabio et a Fumarola, Donato Malerba
Verlag Springer, Berlin
 
Sprache Englisch
Produktform Taschenbuch
Erschienen 20.08.2013
 
EAN 9781447154532
ISBN 978-1-4471-5453-2
Seiten 105
Abmessung 156 mm x 239 mm x 8 mm
Illustration XIII, 105 p. 39 illus., 37 illus. in color.
Serien SpringerBriefs in Computer Science
Springerbriefs in Computer Sci
SpringerBriefs in Computer Science
Thema Naturwissenschaften, Medizin, Informatik, Technik > Informatik, EDV > Informatik

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