Distributed LMS for Consensus-Based In-Network Adaptive Processing

Schizas, Ioannis D.; Mateos, Gonzalo; Giannakis, Georgios B.

doi:10.1109/tsp.2009.2016226

Cited by 326 publications

(223 citation statements)

References 16 publications

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“…where Pr(g i−1 ) is a priori probability and where the probability Pr(g i (l) | g i−1 (l)) is determined by (23). Note that for a static network, the transition probability p n,m is independent of i, i.e., the Markov chain is homogeneous [62].…”

Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

Distributed Decision-Making Over Adaptive Networks

Sayed

2014

IEEE Trans. Signal Process.

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In distributed processing, agents generally collect data generated by the same underlying unknown model (represented by a vector of parameters) and then solve an estimation or inference task cooperatively.In this paper, we consider the situation in which the data observed by the agents may have risen from two different models. Agents do not know beforehand which model accounts for their data and the data of their neighbors. The objective for the network is for all agents to reach agreement on which model to track and to estimate this model cooperatively. In these situations, where agents are subject to data from unknown different sources, conventional distributed estimation strategies would lead to biased estimates relative to any of the underlying models. We first show how to modify existing strategies to guarantee unbiasedness. We then develop a classification scheme for the agents to identify the models that generated the data, and propose a procedure by which the entire network can be made to converge towards the same model through a collaborative decision-making process. The resulting algorithm is applied to model fish foraging behavior in the presence of two food sources.

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Section: Appendix B Proof Of Theoremmentioning

confidence: 99%

Distributed Decision-Making Over Adaptive Networks

Sayed

2014

IEEE Trans. Signal Process.

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“…Adaptive algorithms based on in-network processing of distributed observations are wellmotivated for online parameter estimation and tracking of (non)stationary signals using peer-to-peer WSNs. To this end, a fully distributed least mean-square (D-LMS) algorithm is developed in (Schizas et al, 2009), offering simplicity and flexibility while solely requiring single-hop communications among sensors. The resultant estimator minimizes a pertinent squared-error cost by resorting to i) the alternating-direction method of multipliers so as to gain the desired degree of parallelization and ii) a stochastic approximation iteration to cope with the time-varying statistics of the process under consideration.…”

Section: Alternating-direction Based Consensusmentioning

confidence: 99%

Target Tracking in Wireless Sensor Networks

Li¹,

Zhou²

2010

Wireless Sensor Networks: Application-Centric Design

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“…Alternatively, fully distributed strategies [8][9][10][11][12][13] (i.e., without a FC) where the SNs exchange local information iteratively with their neighbors, are shown to be capable of reaching a global optimum decision. Reference [8] adopts the diffusion LMS approach while [10][11][12][13] adopt the distributed consensus algorithm [14].…”

Section: Introductionmentioning

confidence: 99%

“…Reference [8] adopts the diffusion LMS approach while [10][11][12][13] adopt the distributed consensus algorithm [14]. All these works assume ideal exchange of information among the SNs, but as the SNs are battery operated (i.e., limited energy) this assumption is unrealistic.…”

Section: Introductionmentioning

confidence: 99%

Distributed detection in practical wireless sensor networks via a two-step consensus algorithm

Nurellari

McLernon

Ghogho

2015

2nd IET International Conference on Intelligent Signal Processing 2015 (ISP)

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The problem of fully distributed detection of an unknown deterministic signal observed by a wireless sensor network (WSN) is addressed. We propose a two-step distributed consensus-based detection algorithm where in the first step the sensor nodes (SNs) collaborate with their neighbors through error-free, orthogonal channels (the SNs exchange quantized information matched to the channel capacity of each link). In the second step, local 1-bit decisions generated in the first step are shared among neighbors to yield a consensus. Simulations show that our proposed quantized two-step distributed detection algorithm approaches the performance of the unquantized centralized (fusion center) detector.

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Distributed LMS for Consensus-Based In-Network Adaptive Processing

Cited by 326 publications

References 16 publications

Distributed Decision-Making Over Adaptive Networks

Distributed Decision-Making Over Adaptive Networks

Target Tracking in Wireless Sensor Networks

Distributed detection in practical wireless sensor networks via a two-step consensus algorithm

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