Filtering approaches to accelerated consensus in diffusion sensor networks

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.