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Computer Science > Distributed, Parallel, and Cluster Computing

arXiv:1302.5371 (cs)
[Submitted on 21 Feb 2013]

Title:Non-Linear Distributed Average Consensus using Bounded Transmissions

Authors:Sivaraman Dasarathan, Cihan Tepedelenlioglu, Mahesh Banavar, Andreas Spanias
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Abstract:A distributed average consensus algorithm in which every sensor transmits with bounded peak power is proposed. In the presence of communication noise, it is shown that the nodes reach consensus asymptotically to a finite random variable whose expectation is the desired sample average of the initial observations with a variance that depends on the step size of the algorithm and the variance of the communication noise. The asymptotic performance is characterized by deriving the asymptotic covariance matrix using results from stochastic approximation theory. It is shown that using bounded transmissions results in slower convergence compared to the linear consensus algorithm based on the Laplacian heuristic. Simulations corroborate our analytical findings.
Comments: 24 pages, 8 figures, Submitted to IEEE Transactions on Signal Processing
Subjects: Distributed, Parallel, and Cluster Computing (cs.DC); Information Theory (cs.IT)
Cite as: arXiv:1302.5371 [cs.DC]
  (or arXiv:1302.5371v1 [cs.DC] for this version)
  https://doi.org/10.48550/arXiv.1302.5371
Related DOI: https://doi.org/10.1109/TSP.2013.2282912

Submission history

From: Sivaraman Dasarathan [view email]
[v1] Thu, 21 Feb 2013 18:42:51 UTC (3,455 KB)
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Sivaraman Dasarathan
Cihan Tepedelenlioglu
Mahesh K. Banavar
Andreas Spanias
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