Distributed Clustering Using Wireless Sensor Networks

Forero, Pedro A.; Cano, Alfonso; Giannakis, Georgios B.

doi:10.1109/jstsp.2011.2114324

Cited by 161 publications

(125 citation statements)

References 23 publications

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“…As expected, c-KL with t kernel outperforms c-KL with g kernel slightly. The distortions of our distributed algorithm (with diffusion cooperation) are very close to and sometimes even lower than (similar phenomena are also found in studies on distributed clustering [25,28]) that of the corresponding centralized algorithm. Besides, its performances are hardly affected by the node unbalance.…”

Section: Resultssupporting

confidence: 85%

“…Therefore, distributed signal processing algorithms, which take these limitations into consideration, are needed in such cases. Many distributed algorithms have been proposed in recent years [10], such as the distributed parameter estimation [11][12][13][14][15][16][17][18][19][20], distributed Kalman filtering [21,22], distributed detection [23,24], distributed clustering [25,26], and distributed information-theoretic learning [27,28]. In a majority of these distributed algorithms, signal processing tasks are accomplished at each node based on local computation, local data, as well as limited information exchange among neighbor nodes.…”

Section: Introductionmentioning

confidence: 99%

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Distributed Vector Quantization Based on Kullback-Leibler Divergence

Shen

Luo

2015

Entropy

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Abstract:The goal of vector quantization is to use a few reproduction vectors to represent original vectors/data while maintaining the necessary fidelity of the data. Distributed signal processing has received much attention in recent years, since in many applications data are dispersedly collected/stored in distributed nodes over networks, but centralizing all these data to one processing center is sometimes impractical. In this paper, we develop a distributed vector quantization (VQ) algorithm based on Kullback-Leibler (K-L) divergence. We start from the centralized case and propose to minimize the K-L divergence between the distribution of global original data and the distribution of global reproduction vectors, and then obtain an online iterative solution to this optimization problem based on the Robbins-Monro stochastic approximation. Afterwards, we extend the solution to apply to distributed cases by introducing diffusion cooperation among nodes. Numerical simulations show that the performances of the distributed K-L-based VQ algorithm are very close to the corresponding centralized algorithm. Besides, both the centralized and distributed K-L-based VQ show more robustness to outliers than the (centralized) Linde-Buzo-Gray (LBG) algorithm and the (centralized) self-organization map (SOM) algorithm.

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Section: Resultssupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Shen

Luo

2015

Entropy

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“…kernel based fuzzy c-means and fuzzy clustering (KFCM-F) [17] (c) parallel and distributed clustering methods viz. parallel fuzzy cmeans(PFCM) [18], soft distributed k-means (Soft-DKM) [19] and kernel based collaborative distributed fuzzy c-means (KCDFCM) [20]. The algorithms are implemented in MATLAB with 3.6 GHz CPU and 6 GB RAM.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Kashef et al [8] presented a distributed cooperative clustering method in a two tier hierarchical P2P network. Forero et al [9] proposed a good solution of distributed clustering in WSNs by capitalizing on the 16th World Congress of the International Fuzzy Systems Association (IFSA) 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) consensus based formulation and parallel optimization tools. Pedrycz et al [10] introduced the concept of collaborative fuzzy clustering where the summarized knowledge structures in different peers are shared by communicating information granules.…”

Section: Related Workmentioning

confidence: 99%

Hierarchical Intuitionistic Fuzzy Possibilistic C Means Kernel Clustering Algorithm for Distributed Networks

Chaudhuri

Ghosh

2015

Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Advances in distributed networking have resulted in an explosion in size of modern datasets while storage and processing power continue to lag behind. This requires the need for algorithms that are efficient in terms of number of measurements and running time. To combat challenges associated with large datasets in distributed networks we propose hierarchical intuitionistic fuzzy possibilistic c-means kernel clustering algorithm. The algorithm executes hierarchically by performing clustering at each peer. The intuitionistic fuzzy degree and tipicality membership functions and weight-attributeentropy factor improves clustering performance. The experiments on artificial and real datasets establish the efficiency and effectiveness of the algorithm.

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“…For a comprehensive review of consensus and gossip, the reader is directed to Garin and Schenato (2011) and the references therein. Examples of algorithms distributed using consensus are the Kalman filter (Olfati-Saber, 2005), detection (Bajovic, Jakovetic, Xavier, Sinopoli, & Moura, 2011), clustering (Forero, Cano, & Giannakis, 2011), support vector machines (Forero, Cano, & Giannakis, 2010), linear discriminant analysis (Valcarcel Macua, Belanovic, & Zazo, 2011), and many others. In this work we explore the application of consensus algorithms to SLGMs.…”

Section: Related Workmentioning

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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Distributed Clustering Using Wireless Sensor Networks

Cited by 161 publications

References 23 publications

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Distributed Vector Quantization Based on Kullback-Leibler Divergence

Hierarchical Intuitionistic Fuzzy Possibilistic C Means Kernel Clustering Algorithm for Distributed Networks

Distributed static linear Gaussian models using consensus

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