Efficient and Fault-Tolerant Feature Extraction in Wireless Sensor Networks

We consider a canonical task in wireless sensor networks - the extraction of information about environmental features - and propose a multi-step solution that is fault-tolerant, self-organizing and energy-efficient. We explicitly take into account the possibility of sensor measurement faults and study a distributed algorithm for detecting and correcting such faults, showing through theoretical analysis and simulation results that 85-95% of faults can be corrected using this algorithm even when as many as 10% of the nodes are faulty.We present a self-organizing algorithm which combines shortest-path routing mechanisms with leader-election to permit nodes within each feature region to self-organize into routing clusters. These clusters are used in data aggregation schemes that we propose for feature extraction. We show that the best such aggregation scheme can result in an order-of-magnitude improvement in energy savings.