Michael G. Rabbat

  1. Multiscale Gossip for Efficient Decentralized Averaging in Wireless Packet Networks.

    Authors: Michael G. Rabbat, Konstantinos I. Tsianos
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    This paper describes and analyzes a hierarchical gossip algorithm for solving
    the distributed average consensus problem in wireless sensor networks. The
    network is recursively partitioned into subnetworks. Initially, nodes at the
    finest scale gossip to compute local averages. Then, using geographic routing
    to enable gossip between nodes that are not directly connected, these local
    averages are progressively fused up the hierarchy until the global average is
    computed.

  2. Gossip Algorithms for Distributed Signal Processing.

    Authors: Alexandros G. Dimakis, Soummya Kar, Michael G. Rabbat, Jose M.F. Moura, Anna Scaglione
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    Gossip algorithms are attractive for in-network processing in sensor networks
    because they do not require any specialized routing, there is no bottleneck or
    single point of failure, and they are robust to unreliable wireless network
    conditions. Recently, there has been a surge of activity in the computer
    science, control, signal processing, and information theory communities,
    developing faster and more robust gossip algorithms and deriving theoretical
    performance guarantees. This article presents an overview of recent work in the
    area.

  3. Optimization and Analysis of Distributed Averaging with Short Node Memory.

    Authors: Boris N. Oreshkin, Mark J. Coates, Michael G. Rabbat
    Subjects: and Cluster Computing, Distributed, Parallel
    Abstract

    In this paper, we demonstrate, both theoretically and by numerical examples,
    that adding a local prediction component to the update rule can significantly
    improve the convergence rate of distributed averaging algorithms. We focus on
    the case where the local predictor is a linear combination of the node's two
    previous values (i.e., two memory taps), and our update rule computes a
    combination of the predictor and the usual weighted linear combination of
    values received from neighbouring nodes.

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