@conference {
	title = {Consensus-Based Linear Distributed Filtering},
	author = {Matei, Ion., Baras, John S.},
	year = {2010},
	month = {12/2010},
	abstract = {We address the consensus-based distributed linear filtering problem, where a discrete time, linear stochastic process is observed by a network of sensors. We assume that the consensus weights are known and we first provide sufficient conditions under which the stochastic process is detectable, i.e. for a specific choice of consensus weights there exists a set of filtering gains such that the dynamics of the estimation errors (without noise) is asymptotically stable. Next, we provide a distributed, sub-optimal filtering scheme based on minimizing an upper bound on a quadratic filtering cost. In the stationary case, we provide sufficient conditions under which this scheme converges; conditions expressed in terms of the convergence properties of a set of coupled Riccati equations. We continue with presenting a connection between the consensus based distributed linear filter and the optimal linear filter of a Markovian jump linear system, appropriately defined. More specifically, we show that if the Markovian jump linear system is (mean square) detectable, then the stochastic process is detectable under the consensus-based distributed linear filtering scheme. We also show that the optimal gains of a linear filter for estimating the state of a Markovian jump linear system appropriately defined can be used to approximate the optimal gains of the consensus-based linear filter.},
	address = {Atlanta, Georgia},
	booktitle = {49th IEEE Conference on Decision and Control (CDC 2010)},
	pages = {pp.  7009- 7014}
}
@conference {
	title = {The asymptotic consensus problem on convex metric spaces},
	author = {Matei, Ion., Baras, John S.},
	year = {2010},
	month = {09/2010},
	abstract = {We consider the consensus problem of a group of dynamic agents whose communication network is modeled by a directed time-varying graph. In this paper we generalize the asymptotic consensus problem to convex metric spaces. A convex metric space is a metric space endowed with a convex structure. Using this convex structure we define convex sets and in particular the convex hull of a (finite) set. Under minimal connectivity assumptions, we show that if at each iteration an agent updates its state by choosing a point from a particular subset of the convex hull generated by the agent{\textquoteright}s current state and the states of its neighbors, then asymptotic agreement is achieved. In addition, we give bounds on the distance between the consensus point and the initial values of the agents.},
	address = {Annecy, France},
	booktitle = {2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys{\textquoteright}10)},
	pages = {pp. 287- 292}
}
@conference {
	title = {Performance Evaluation of Multi-Agent Distributed Collaborative Optimizationunder Random Communication Topologies},
	author = {Matei, Ion., Baras, John S.},
	year = {2010},
	month = {07/2010},
	abstract = {We investigate collaborative optimization in a multi-agent setting, when the agents execute in a distributed manner using local information, while the communication topology used to exchange messages and information is modeled by a graph-valued random process, independent of other time instances. Specifically, we study the performance of the consensus-based multi-agent subgradient method, for the case of a constant stepsize, as measured by two metrics: rate of convergence and guaranteed region of convergence, evaluated via their expected values. Under a strong convexity type of assumption, we provide upper bounds on the performance metrics, which explicitly depend on the probability distribution of the random graph and on the agents{\textquoteright} estimates of the optimal solution. This provides a guide for tuning the parameters of the communication protocol such that good performance of the multi-agent subgradient method is ensured.},
	address = {Budapest, Hungary},
	booktitle = {Proceedings of the19th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2010)},
	pages = {pp.1-8}
}
@conference {
	title = {A Trust Based Distributed Kalman Filtering Approach for Mode Estimation in Power Systems},
	author = {Jiang, Tao., Matei, Ion., Baras, John S.},
	year = {2010},
	month = {04/2010},
	abstract = {We consider distributed mode estimation in power systems. The measurements are observed by PMUs (Power Management Units). We introduce a novel model of trust, using weights on the graph links and nodes that represent the networked PMUs. We describe two algorithms that intergarte distributed Kalman filtering with these trust weights. We consider two interpretations of these trust weights as information accuracy and reliability. We show that by appropriate use of these weights from the distributed estimation algorithm avoids using information from untrusted PMUs. Simulation experiments further demonstrate the behavior of these algorithms.},
	address = {Stockholm, Sweden},
	booktitle = {First Workshop on Secure Control Systems (SCS) as part of CPSWeek 2010},
	pages = {pp. 1-6}
}
