Title :
Topology for Distributed Inference on Graphs
Author :
Kar, Soummya ; Aldosari, Saeed ; Moura, José M F
Author_Institution :
Dept. of Electr. & Comput. Eng., Carnegie Mellon Univ., Pittsburgh, PA
fDate :
6/1/2008 12:00:00 AM
Abstract :
Let N decision-makers collaborate to reach a decision. We consider iterative distributed inference with local intersensor communication, which, under simplifying assumptions, is equivalent to distributed average consensus. We show that, under appropriate conditions, the topology given by the nonbipartite Ramanujan graphs optimizes the convergence rate of this distributed algorithm.
Keywords :
convergence of numerical methods; decision making; distributed algorithms; distributed sensors; graph theory; iterative methods; telecommunication network topology; decision making; distributed average consensus; iterative distributed inference algorithm convergence; local intersensor communication; nonbipartite Ramanujan graph; sensor network topology design; Algorithm design and analysis; Collaboration; Communication channels; Convergence; Distributed algorithms; Graph theory; Inference algorithms; Iterative algorithms; Laplace equations; Network topology; Algebraic connectivity; Cayley; Laplacian; Ramanujan; consensus algorithm; distributed detection; random graphs; sensor networks; small-world; spectral graph theory; topology optimization;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2008.923536
