Journal ArticleDOI
Estimation on graphs from relative measurements
Prabir Barooah,Joao P. Hespanha +1 more
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This work studied one of these problems in terms of how such an estimate can be efficiently computed in a distributed manner as well as how the quality of an optimal estimate scales with the size of the network.Abstract:
Large-scale sensor networks give rise to estimation problems that have a rich graphical structure. We studied one of these problems in terms of how such an estimate can be efficiently computed in a distributed manner as well as how the quality of an optimal estimate scales with the size of the network. Two distributed algorithms are presented to compute the optimal estimates that are scalable and robust to communication failures. In designing these algorithms, we found the literature on parallel computation to be a rich source of inspiration.read more
Citations
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Journal ArticleDOI
Coherence in Large-Scale Networks: Dimension-Dependent Limitations of Local Feedback
TL;DR: It is shown that it is impossible to have large coherent 1-D vehicular platoons with only local feedback, and that in low spatial dimensions, local feedback is unable to regulate large-scale disturbances, but it can in higher spatial dimensions.
Journal ArticleDOI
Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms and Asymptotic Behavior
Minyi Huang,Jonathan H. Manton +1 more
TL;DR: In this paper, the authors consider the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors' states and propose stochastic approximation-type algorithms with a decreasing step size, and introduce the notions of mean square and strong consensus.
Coordination and Consensus of Networked Agents with Noisy Measurements: Stochastic Algorithms
Minyi Huang,Jonathan H. Manton +1 more
TL;DR: This paper considers the coordination and consensus of networked agents where each agent has noisy measurements of its neighbors' states, and proposes stochastic approximation-type algorithms with a decreasing step size, and introduces the notions of mean square and strong consensus.
Journal ArticleDOI
Electrical Networks and Algebraic Graph Theory: Models, Properties, and Applications
TL;DR: This paper surveys some fundamental and historic as well as recent results on how algebraic graph theory informs electrical network analysis, dynamics, and design, and reviews the algebraic and spectral properties of graph adjacency, Laplacian, incidence, and resistance matrices.
Journal ArticleDOI
Algorithms for Leader Selection in Stochastically Forced Consensus Networks
TL;DR: This work shows that the Boolean constraints (which indicate whether a node is a leader) are the only source of nonconvexity, and develops a customized algorithm well-suited for large networks.
References
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Book
Algebraic Graph Theory
Chris Godsil,Gordon F. Royle +1 more
TL;DR: The Laplacian of a Graph and Cuts and Flows are compared to the Rank Polynomial.
Book
Graduate Texts in Mathematics
Rajendra Bhatia,Glen Bredon,Wolfgang Walter,Joseph J. Rotman,M. Ram Murty,Jane Gilman,Peter Walters,Martin Golubitsky,Ioannis Karatzas,Henri Cohen,Raoul Bott,Gaisi Takeuti,Béla Bollobás,John M. Lee,Jiří Matoušek,Saunders Mac Lane,John L. Kelley,B. A. Dubrovin,Tom M. Apostol,John Stillwell,William Arveson +20 more
Book
Random walks and electric networks
Peter G. Doyle,J. Laurie Snell +1 more
TL;DR: The goal will be to interpret Polya’s beautiful theorem that a random walker on an infinite street network in d-dimensional space is bound to return to the starting point when d = 2, but has a positive probability of escaping to infinity without returning to the Starting Point when d ≥ 3, and to prove the theorem using techniques from classical electrical theory.
Journal ArticleDOI
Connecting the physical world with pervasive networks
TL;DR: This article addresses the challenges and opportunities of instrumenting the physical world with pervasive networks of sensor-rich, embedded computation with a taxonomy of emerging systems and outlines the enabling technological developments.
Book
Lessons in Estimation Theory for Signal Processing, Communications, and Control
TL;DR: In this paper, the authors present an overview of the state-of-the-art algorithms for least-squares estimators and their applications in higher-order statistics, including iterated least squares and extended Kalman-Bucy filtering.
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