Data versus decision fusion in wireless sensor networks

Abstract

Sensor networks provide virtual snapshots of the physical world via distributed wireless nodes that can sense in different modalities, such as acoustic and seismic. Classification of objects moving through the sensor field is an important application that requires collaborative signal processing (CSP) between nodes. Given the limited resources of nodes, a key constraint is to exchange the least amount of information between them to achieve the desired performance. Two main forms of CSP are possible. Data fusion - exchange of low dimensional feature vectors - is needed between correlated nodes, in general, for optimal performance. Decision fusion $exchange of likelihood values - is sufficient between independent nodes. Decision fusion is generally preferable due to its lower communication burden. We study the CSP of multiple node measurements, each modeled as a Gaussian signal vector (corresponding to the target class) corrupted by additive white Gaussian noise. The measurements are partitioned into groups. The signal components within each group are perfectly correlated whereas they vary independently between groups. Three classifiers are compared: the optimal maximum likelihood classifier; a data averaging classifier that treats all measurements as correlated; a decision fusion classifier that treats them all as independent. The performances of the three CSP classifiers are compared using analytical and numerical results based on real data. These indicate that the sub-optimal decision fusion classifier, that is most attractive in the context of sensor networks, is also a robust choice from a decision theoretic viewpoint.