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Filtering approaches to accelerated consensus in diffusion sensor networks
Author(s) -
AbdElrady Emad,
Mulgrew Bernard
Publication year - 2014
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.2540
Subject(s) - subgradient method , computer science , affine transformation , mathematical optimization , iterative method , wireless sensor network , algorithm , adaptive filter , network topology , mathematics , machine learning , pure mathematics , operating system , computer network
SUMMARY The main objective in distributed sensor networks is to reach agreement or consensus on values acquired by the sensors. A common methodology to approach this problem is using the iterative and weighted linear combination of those values to which each sensor has access. Different methods to compute appropriate weights have been extensively studied, but the resulting iterative algorithm still requires many iterations to provide a fairly good estimate of the consensus value. In this paper, different accelerating consensus approaches based on adaptive and non‐adaptive filtering techniques are studied and applied on the problem of acoustic source localization using the adaptive projected subgradient method. A comparative simulation study shows that the non‐adaptive polynomial filters based on Newton's interpolating polynomials and semi‐definite programming can provide more accelerated consensus and better estimation accuracy than adaptive filters evaluated using constrained affine projection algorithm or stochastic gradient algorithm provided that the network topology is known beforehand. Copyright © 2013 John Wiley & Sons, Ltd.