Database Reference
In-Depth Information
Chapter 8
DISTRIBUTED DATA MINING IN SENSOR
NETWORKS
Kanishka Bhaduri
Netflix Inc.
100 Winchester Circle
Los Gatos, CA 94032
kanishka.bh@gmail.com
Marco Stolpe
TU Dortmund University
Artificial Intelligence Group, LS 8
Joseph-von-Fraunhofer-Straße 23
44227, Dortmund, Germany
marco.stolpe@tu-dortmund.de
Abstract
Wireless sensor networks (WSNs) consist of a collection of low cost
and low powered sensor devices capable of communicating with each
other via an ad-hoc wireless network. Due to their rapid proliferation,
sensor networks are currently used in a plethora of applications such as
earth sciences, systems health, military applications etc. These sensors
collect the data about the environment and this data can be mined for
a variety of analysis. Unfortunately, post analysis of the data extracted
from the WSN incurs high sensor communication cost for sending the
raw data to the base station and at the same time runs the risk of
delayed analysis. To overcome this, researchers have proposed several
distributed algorithms which can deal with the data in situ - these data
mining algorithms utilize the computing power at each node to first do
some local computations and then exchange messages with its neighbors
to come to a consensus regarding a global model. These algorithms
reduce the communication cost vastly and also are extremely ecient
in terms of model computation and event detection. In this chapter we
focus on such distributed data mining algorithms for data clustering,
classification and outlier detection tasks.
Keywords:
distributed data mining, sensor networks, outlier detection
