Adaptive data stream mining for wireless sensor networks

Data stream mining in wireless sensor networks has many important applications. Realizing these applications is faced by resource constraints of the sensor nodes that form the network. Adaptation to availability of resources is crucial to the success of these applications. In this paper, we propose a distributed data stream classification technique that has been tested on a real sensor network platform, namely, Sun SPOT. Experimental results evidenced the applicability of our technique to operate in such an environment of scarce resources.