Distributed Data Mining in Sensor Networks

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 efficient 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.