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Broadcast Detection Structures with Applications to Sensor Networks

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Title: Broadcast Detection Structures with Applications to Sensor Networks;
Distributed Structures, Sequential Optimization, and Quantization for Detection
Author: Johnson, Don; Lexa, Michael
Type: Journal Paper
Keywords: Distributed detection; sensor networks; Kullback-Leibler divergence; broadcast detection structures
Citation: D. Johnson and M. Lexa, "Broadcast Detection Structures with Applications to Sensor Networks and Distributed Structures, Sequential Optimization, and Quantization for Detection," IEEE Transactions on Signal Processing, 2006 and 2007.
Abstract: 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.
Date Published: 2006-03-01

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  • ECE Publications [1032 items]
    Publications by Rice University Electrical and Computer Engineering faculty and graduate students
  • DSP Publications [508 items]
    Publications by Rice Faculty and graduate students in digital signal processing.