Distributed Connectivity Optimization in Asymmetric Networks

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Authors
  1. Asadi, M.M.
  2. Blouin, S.
  3. Aghdam, A.G.
Corporate Authors
Defence Research and Development Canada, Atlantic Research Centre, Halifax NS (CAN);Concordia Univ, Montreal QUE (CAN) Dept of Electrical and Computer Engineering
Abstract
The problem of distributed connectivity optimization of an asymmetric sensor network represented by a weighted directed graph (digraph) is investigated in this paper. The notion of generalized algebraic connectivity is used to measure the connectivity of a time-varying weighted digraph. The generalized algebraic connectivity is regarded as a nonconcave and nondifferentiable continuous cost function, and a distributed approach, based on the subspace consensus algorithm, is developed to compute the supergradient vector of the network connectivity. By considering the above-mentioned network connectivity as a function of the transmission power vector of the network, a discrete-time update procedure is proposed to compute a stationary transmission power vector of the network which locally maximizes the network connectivity. The effectiveness of the developed algorithm is subsequently demonstrated by simulations.
Keywords
network;connectivity;optimization
Report Number
DRDC-RDDC-2018-P026 — External Literature
Date of publication
01 Mar 2018
Number of Pages
10
Reprinted from
2017 IEEE 56th Annual Conference on Decision and Control (CDC), p. 82-87
DSTKIM No
CA045905
CANDIS No
806360
Format(s):
Electronic Document(PDF)

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