Channel-Aware Distributed Classification in Wireless Sensor Networks Using Binary Local Decisions

This paper considers the problem of distributed multi-hypothesis classification in the context of wireless sensor networks. The goal is to reliably classify an underlying hypothesis at a fusion center using simple localized decisions at individual sensors. The fusion-center classification must be performed despite the presence of faults in both local sensor decisions and transmission channels between the sensors and fusion center. Local sensor nodes make binary classifications based on their noisy observations and send their decisions to the fusion center through parallel additive white Gaussian noise channels. The fusion center then uses these noisy versions of local decisions to perform a global classification.