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. In contrast with other similar approaches for multi-hypothesis classification based on combined binary decisions, our approach exploits the relationship between the influence fields of different hypotheses and the accumulated noisy versions of local binary decisions as received by the fusion center, where the influence field of a hypothesis is defined to be the spatial region in its surrounding in which it can be sensed using some specific modality. The main contribution of this paper is the formulation of local and fusion decision rules that maximize the probability of correct global classification at the fusion center, along with an algorithm for channel-aware global optimization of the local and fusion center decision thresholds. The performance of the proposed classification system is investigated through practical scenarios. Performance analysis results show that the proposed approach could simplify decision making at local sensors while achieving acceptable performance in terms of the global probability of correct classification at the fusion center.