Modeling and Mitigating Errors in Belief Propagation for Distributed Detection

We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by...

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Bibliographic Details
Published in:IEEE transactions on communications Vol. 69; no. 5; pp. 3286 - 3297
Main Authors: Abdi, Younes, Ristaniemi, Tapani
Format: Journal Article
Language:English
Published: New York IEEE 01.05.2021
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Algorithms
blind signal processing
communication errors
computation errors
cooperative communications
Data exchange
Data integration
Decision analysis
Distributed systems
factor graphs
Fields (mathematics)
Hardware
Light rail systems
likelihood-ratio test
linear data-fusion
message-passing algorithms
Optimization
Propagation
Random variables
Sensors
Signal processing algorithms
Stochastic processes
Wireless sensor networks
ISSN:0090-6778, 1558-0857
Online Access:Get full text
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Summary:We study the behavior of the belief-propagation (BP) algorithm affected by erroneous data exchange in a wireless sensor network (WSN). The WSN conducts a distributed multidimensional hypothesis test over binary random variables. The joint statistical behavior of the sensor observations is modeled by a Markov random field whose parameters are used to build the BP messages exchanged between the sensing nodes. Through linearization of the BP message-update rule, we analyze the behavior of the resulting erroneous decision variables and derive closed-form relationships that describe the impact of stochastic errors on the performance of the BP algorithm. We then develop a decentralized distributed optimization framework to enhance the system performance by mitigating the impact of errors via a distributed linear data-fusion scheme. Finally, we compare the results of the proposed analysis with the existing works and visualize, via computer simulations, the performance gain obtained by the proposed optimization.
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ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2021.3056679