@conference {
	title = {Power Allocation Policy for Distributed Estimation in Wireless Networks},
	author = {Matei, Ion., Baras, John S.},
	year = {2011},
	month = {12/2011},
	abstract = {We consider a sensor network monitoring a stochastic process. The sensors exchange information over wireless communication channels affected by noise. The goal of the sensors is to compute accurate estimates of the state of the stochastic process under limited energy available for communications. We introduce a distributed algorithm which computes a power allocation scheme aimed at ensuring accurate state estimates and at saving communication energy. The distributed algorithm arise from solving an approximate convex optimization problem with constraints. We present how the power allocation scheme can be used for performing distributed estimation in the case where measurements are shared between sensors.},
	address = {Orlando, FL},
	booktitle = {50th IEEE Conference on Decision and Control Conference (CDC-ECC 2011)},
	pages = {pp. 7275-7280}
}
@conference {
	title = {Convergence results for the agreement problem on Markovian random topologies},
	author = {Matei, Ion., Baras, John S.},
	year = {2011},
	month = {08/2011},
	abstract = {We study the linear distributed asymptotic agreement(consensus) problem for a network of dynamic agents whose communication network is modeled by a randomly switching graph. The switching is determined by a finite state, Markov process, each topology corresponding to a state of the process. We address both the cases where the dynamics of the agents is expressed in continuous and discrete time. We show that, if the consensus matrices are doubly stochastic, convergence to average consensus is achieved in the mean square and almost sure sense, if and only if the graph resulted from the union of graphs corresponding to the states of the Markov process is strongly connected. The aim of this paper is to show how techniques from the theory of Markovian jump linear systems, in conjunction with results inspired by matrix and graph theory, can be used to prove convergence results for stochastic consensus problems.},
	address = {Milano, Italy},
	booktitle = {2011 IFAC World Conference},
	pages = {pp. 8860-8865}
}
@conference {
	title = {The Geometry of a Probabilistic Consensus of Opinion Algorithm},
	author = {Matei, Ion., Baras, John S.},
	year = {2011},
	month = {06/2011},
	abstract = {We consider the problem of a group of agents whose objective is to asymptotically reach agreement of opinion. The agents exchange information subject to a communication topology modeled by a time-varying graph. The agents use a probabilistic algorithm under which at each time instant an agent updates its state by probabilistically choosing from its current state/opinion and the ones of its neighbors. We show that under some minimal assumptions on the communication topology (infinitely often connectivity and bounded intercommunication time between agents), the agents reach agreement with probability one. We show that this algorithm has the same geometric properties as the linear consensus algorithm in Rn. More specifically, we show that the probabilistic update scheme of an agent is equivalent to choosing a point from the (generalized) convex hull of its current state and the states of its neighbors; convex hull defined on a particular convex metric space where the states of the agents live and for which a detailed description is given.},
	address = {San Francisco, CA},
	booktitle = {2011 American Control Conference},
	pages = {pp.  2198-2205}
}
