Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals

Schizas, Ioannis D.; Giannakis, Georgios B.; Roumeliotis, Stergios I.; Ribeiro, Alejandro

doi:10.1109/tsp.2007.908943

Cited by 176 publications

(144 citation statements)

References 17 publications

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“…Hence, we do not examine here the influence of noisy links, switching topologies, and delay and quantization effects. They have been extensively analyzed in the literature (Kar & Moura, 2009;OlfatiSaber & Murray, 2004;Schizas, Giannakis, Roumeliotis, & Ribeiro, 2008; in generic terms, and their influence on our algorithms is beyond the scope of this paper.…”

Section: Methodsmentioning

confidence: 99%

Distributed static linear Gaussian models using consensus

2012

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Algorithms for distributed agreement are a powerful means for formulating distributed versions of existing centralized algorithms. We present a toolkit for this task and show how it can be used systematically to design fully distributed algorithms for static linear Gaussian models, including principal component analysis, factor analysis, and probabilistic principal component analysis. These algorithms do not rely on a fusion center, require only low-volume local (1-hop neighborhood) communications, and are thus efficient, scalable, and robust. We show how they are also guaranteed to asymptotically converge to the same solution as the corresponding existing centralized algorithms. Finally, we illustrate the functioning of our algorithms on two examples, and examine the inherent cost-performance tradeoff.

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Section: Methodsmentioning

confidence: 99%

Distributed static linear Gaussian models using consensus

2012

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“…where B ⊆ [1, J] is the bridge sensor subset introduced in [5] which is defined by the following pair of conditions: (i) ∀ j ∈ [1, J] there exists at least one b ∈ B such that b ∈ Nj (the bridge neighbors of sensor j will be denoted by Bj := Nj ∩ B); and, (ii) ∀ b1 ∈ B there exists another sensor b2 ∈ B such that the shortest path between b1 and b2 has at most two edges. For the WSN in Fig.…”

Section: The Alternating-direction Methods Of Multipliersmentioning

confidence: 99%

“…Each sensor b ∈ B maintains a local vectorχ b via which consensus among local variables χ j across all sensors is achieved a fortiori. Specifically, we have proved in [5] that the two conditions associated with B, and combined with the connectivity of the WSN, provide a necessary and sufficient condition ensuring that the constraint set in (16) implies…”

Section: The Alternating-direction Methods Of Multipliersmentioning

confidence: 99%

“…We take advantage of the inherent delay present in the consensus phase of the existing distributed KF approaches in order to build a distributed Kalman smoother (KS) that trades-off estimation delay for estimation quality (MSE). We utilize the alternating-direction method of multipliers [3,5] to obtain 'consensus-enriched' observations across sensors and develop judicious local KS recursions enabling each sensor to form any-time MMSE optimal filtered and smoothed state estimates. Further, the distributed smoother is shown resilient to communication noise [5].…”

Section: Introductionmentioning

confidence: 99%

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Anytime Optimal Distributed Kalman Filtering and Smoothing

Schizas

Giannakis

Roumeliotis

et al. 2007

2007 IEEE/SP 14th Workshop on Statistical Signal Processing

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Distributed algorithms are derived for estimation and smoothing of nonstationary dynamical processes based on correlated observations collected by ad hoc wireless sensor networks (WSNs). Specifically, distributed Kalman filtering (KF) and smoothing schemes are constructed for any-time minimum mean-square error (MMSE) optimal consensus-based state estimation using WSNs. The novel distributed filtering/smoothing approach is flexible to trade-off estimation delay for MSE reduction, while it exhibits robustness in the presence of communication noise. Numerical examples demonstrate the merits of the proposed approach with respect to existing alternatives.

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“…The estimator presented in this paper is related to contributions on low-pass filtering by diffusion mechanisms, e.g., [1][2][3][4][5][6][7][8], where each node of the network obtains the average of the initial samples collected by nodes. In [9,10] the authors study a distributed average computation of a time-varying signal, when the signal is affected by a zero-mean noise.…”

Section: Introductionmentioning

confidence: 99%

Model based peer-to-peer estimator over wireless sensor networks with lossy channels

Fischione

Speranzon³

2015

Automatica

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In this paper, the design methods and fundamental performance analysis of an adaptive peer-to-peer estimator are established for networks exhibiting message losses. Based on a signal state model, estimates are locally computed at each node of the network by adaptively filtering neighboring nodes' estimates and measurements communicated over lossy channels. The computation is based on a distributed optimization approach that guarantees the stability of the estimator while minimizing the estimation error variance. The fundamental performance limitations of the estimator are established based on the variance of the estimation error in relation to the message loss process. Numerical simulations validate the theoretical analysis and illustrate the performance with respect to other estimators.

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Consensus in Ad Hoc WSNs With Noisy Links—Part II: Distributed Estimation and Smoothing of Random Signals

Cited by 176 publications

References 17 publications

Distributed static linear Gaussian models using consensus

Distributed static linear Gaussian models using consensus

Anytime Optimal Distributed Kalman Filtering and Smoothing

Model based peer-to-peer estimator over wireless sensor networks with lossy channels

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