Optimal Distributed Estimation In Clustered Sensor Networks

In a clustered, multi-hop sensor network, a large number of inexpensive, geographically-distributed sensor nodes each make measurements of a source, quantize them into binary sequences, and transmit them over one or more wireless hops to the clusterhead. When all local measurement data has been gathered by the clusterhead, it fuses them into a final estimate about the source. Two sources of error affect the clusterhead's final estimate: (i) local measurement errors made by the sensor nodes because of noisy measurements or unreliable sensors; and (ii) bit errors affecting each hop on the wireless communication channel. Previous work assumed error-free communication or a single-hop cluster. We propose an optimal estimate that accounts for both of these sources of error. We show that this estimate significantly outperforms schemes that consider only the measurement error noise-both in terms of error counts and mean square error.