A classification and comparison of Data Mining algorithms for Wireless Sensor Networks

With the technological developments, the Wireless Sensor Network (WSN) nodes are getting smaller, but WSNs are getting larger, currently containing thousands, possible millions of nodes in the future. Consequently, dealing with the shear volume of data produced by these networks poses a serious challenge, one logically approached through use of Data Mining techniques. This has inspired a body of research which aims to develop new, and adapt existing solutions for Distributed Data Mining (DDM), so that they can be efficiently used in WSN. This paper presents and overview of such approaches. A classification framework is proposed, and selected solutions form the current State-of-the-Art available in open literature are presented, and categorized.