Distributed connectivity optimization in asymmetric networks
Mohammad Mehdi Asadi, Stéphane Blouin, Amir G. Aghdam · 2017
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.