Resource-aware Online Data Mining in Wireless Sensor Networks

Data processing in wireless sensor networks often relies on high-speed data stream input, but at the same time is inherently constrained by limited resource availability. Thus, energy efficiency and good resource management are vital for in-network processing techniques. We propose enabling resource-awareness for in-network processing algorithms by means of a resource monitoring component and designed a corresponding framework. As proof of concept, we implement an online clustering algorithm, which uses the resource monitor to adapt to resource availability, on the Sun SPOT sensor nodes from Sun Microsystem. We refer to this adaptive clustering algorithm as extended resource-aware cluster (ERA-cluster). Finally, we report on the outcome of several experiments to evaluate the validity of our approach in terms of resource adaptiveness and accuracy of the ERA-cluster. Results show that ERA-cluster can effectively adapt to resource availability while maintaining acceptable level of accuracy.