Broadcast Detection Structures with Applications to Sensor Networks
Distributed Structures, Sequential Optimization, and Quantization for Detection
Distributed detection; sensor networks; Kullback-Leibler divergence; broadcast detection structures
Data broadcasting is potentially an effective and efficient way to share information in wireless sensor networks. Broadcasts offer energy savings over multiple, directed transmissions, and they provide a vehicle to exploit the statistical dependencies often present in distributed data. In this paper, we examine two broadcast structures in the context of a distributed detection problem whose inputs are statistically dependent. Specifically, we develop a suboptimal approach to maximize the Kullback-Leibler divergence over a set of binary quantization rules. Our approach not only leads to simple parameterizations of the quantization rules in terms of likelihood ratio thresholds, but also provides insight into the inherent constraints distributed structures impose. We then present two examples in detail and compare the performance of the broadcast structures to that of a centralized system and a noncooperative system. These examples suggest that in situations where the detection problem is difficult (small input divergence), broadcasting solitary bits (or even nothing at all) may be nearly as effective as broadcasting real-valued observations.