Distributed parameter estimation with selective cooperation

Abstract

This paper proposes selective update and cooperation strategies for parameter estimation in distributed adaptive sensor networks. A setmembership filtering approach is employed that results in reduced complexity for updating parameter estimates at each network node, a significant reduction in information exchange between cooperating nodes, and an optimal strategy to obtain consensus estimates. The proposed strategies and the estimation algorithm offer a new way to explore cooperation in adaptive distributed sensor networks.

An SMF approach to distributed average consensus in clustered sensor networks

“…Furthermore, in most of the cases studied, the SMF adaptive algorithms offer estimation performance comparable to those of LMS and RLS which update parameter estimates regardless of the benefits of such updates. This feature has recently been exploited to a great advantage in the studies of distributed estimation [8,9].…”

“…SMF algorithms exhibit a salient feature with which the parameter estimate is updated only when the magnitude of the error exceeds a predefined threshold, which is an indication that the observed data contain sufficient fresh information [3]- [7]. This feature makes the SMF approach a viable candidate for DSN applications where resources have to be utilized efficiently [8,9]. Using SMF, various diffusion strategies based on selective cooperation have been proposed in [9] for distributed parameter estimation.…”

“…This feature makes the SMF approach a viable candidate for DSN applications where resources have to be utilized efficiently [8,9]. Using SMF, various diffusion strategies based on selective cooperation have been proposed in [9] for distributed parameter estimation.…”

Analysis of a reduced-communication diffusion LMS algorithm

“…The diffusion-based adaptive algorithm of [9] mitigates the communication load by exchanging either a scalar or a single information bit generated from random projections of the intermediate estimate vector of each node. The works of [10,11] utilize the concept of set-membership filtering [12] to alleviate the communication cost. In [13][14][15], two lowcommunication algorithms for adaptive distributed estimation are proposed employing the notion of partial diffusion where each node transmits a part of the entries of its intermediate estimate vector to its neighbors at each iteration.…”

Distributed cooperative spectrum sensing with adaptive combining

“…Information centered at only one point would require the fusion center to be able to process a very large amount of data, in addition to being more sensitive to link failures. Furthermore, increasing distance between nodes require the radios to use more power and consequently increases network energy consumption [5]. Therefore, a distributed approach arises as a This work was supported, in part, by the Academy of Finland, Center of Excellence Smart Radios and Wireless Research at the Aalto University, and by CNPq and FAPERJ, Brazil.…”