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Distributed Two-Step Quantized Fusion Rules Via Consensus Algorithm for Distributed Detection in Wireless Sensor Networks
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DOI：10.1109/TSIPN.2016.2549743.png)
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Abstract
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We consider the problem of distributed soft decision fusion in a bandwidth-constrained spatially uncorrelated wireless sensor network (WSN). The WSN is tasked with the detection of an intruder transmitting an unknown signal over a fading channel. Existing distributed consensus-based fusion rules algorithms only ensure equal combining of local data and in the case of bandwidth-constrained WSNs, we show that their performance is poor and does not converge across the sensor nodes (SNs). Motivated by this fact, we propose a two-step distributed quantized fusion rule algorithm where in the first step the SNs collaborate with their neighbors through error-free, orthogonal channels (the SNs exchange quantized information matched to the channel capacity of each link). In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. A binary hypothesis testing is performed at any arbitrary SN to optimally declare the global decision. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (with a fusion center) detector and its power consumption is shown to be 50% less than the existing (unquantized) conventional algorithm.
Keywords:
Distributed detection
soft decision
quantized weighted average consensus
wireless sensor networks
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