Data Mining Techniques in Sensor Networks_ Summarization, Interpolation and Surveillance [Appice, Ciampi, Fumarola & Malerba 2013-09-27](1).pdf

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SPRINGER BRIEFS IN COMPUTER SCIENCE
Annalisa Appice
Anna Ciampi
Fabio Fumarola
Donato Malerba
Data Mining
Techniques in
Sensor Networks
Summarization,
Interpolation and
Surveillance
123
SpringerBriefs in Computer Science
Series Editors
Stan Zdonik
Peng Ning
Shashi Shekhar
Jonathan Katz
Xindong Wu
Lakhmi C. Jain
David Padua
Xuemin Shen
Borko Furht
V. S. Subrahmanian
Martial Hebert
Katsushi Ikeuchi
Bruno Siciliano
For further volumes:
http://www.springer.com/series/10028
Annalisa Appice Anna Ciampi
Fabio Fumarola Donato Malerba
•
•
Data Mining Techniques
in Sensor Networks
Summarization, Interpolation
and Surveillance
123
Annalisa Appice
Anna Ciampi
Fabio Fumarola
Donato Malerba
Dipartimento di Informatica
Università degli Studi di Bari ‘‘Aldo Moro’’
Bari
Italy
ISSN 2191-5768
ISBN 978-1-4471-5453-2
DOI 10.1007/978-1-4471-5454-9
ISSN 2191-5776 (electronic)
ISBN 978-1-4471-5454-9 (eBook)
Springer London Heidelberg New York Dordrecht
Library of Congress Control Number: 2013944777
Ó
The Author(s) 2014
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Preface
Preamble
Sensor networks consist of distributed devices, which monitor an environment by
collecting data (light, temperature, humidity,…). Each node in a sensor network
can be imagined as a small computer, equipped with the basic capacity to sense,
process, and act. Sensors act in dynamic environments, often under adverse
conditions.
Typical applications of sensor networks include monitoring, tracking, and
controlling. Some of the specific applications are photovoltaic plant controlling,
habitat monitoring, traffic monitoring, and ecological surveillance. In these
applications, a sensor network is scattered in a (possibly large) region where it is
meant to collect data through its sensor nodes.
While the technical problems associated with sensor networks have reached
certain stability, managing sensor data brings numerous computational challenges
[1, 5] in the context of data collection, storage, and mining. In particular, learning
from data produced from a sensor network poses several issues: sensors are dis-
tributed; they produce a continuous flow of data, eventually at high speeds; they
act in dynamic, time-changing environments; the number of sensors can be very
large and dynamic. These issues require the design of efficient techniques for
processing data produced by sensor networks. These algorithms need to be exe-
cuted in one step of the data, since typically it is not always possible to store the
entire dataset, because of storage and other constraints.
Processing sensor data has developed new software paradigms, both creating
new techniques or adapting, for network computing, old algorithms of earlier
computing ages [2, 3]. The traditional knowledge discovery environment has been
adapted to process data streams generated from sensor networks in (near) real
time, to raise possible alarms, or to supplement missing data [6]. Consequently, the
development of sensor networks is now accompanied by several algorithms for
data mining which are modified versions of clustering, regression, and anomaly
detection techniques from the field of multidimensional data series analysis in
other scientific fields [4].
The focus of this book is to provide the reader with an idea of
data mining
techniques in sensor networks.
We have taken special care to illustrate the impact
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