@conference {
	title = {Reachability Analysis for Linear Discrete Time Set{\textendash}Dynamics Driven by Random Convex Compact Sets},
	author = {Rakovic, Sasa V., Matei, Ion., Baras, John S.},
	year = {2012},
	month = {12/2012},
	abstract = {This paper studies linear set{\textendash}dynamics driven by random convex compact sets (RCCSs), and derives the set{\textendash}dynamics of the expectations of the associated reach sets as well as the dynamics of the corresponding covariance functions. It is established that the expectations of the reach sets evolve according to deterministic linear set{\textendash}dynamics while the associated dynamics of covariance functions evolves on the Banach space of continuous functions on the dual unit ball. The general framework is specialized to the case of Gaussian RCCSs, and it is shown that the Gaussian structure of random sets is preserved under linear set{\textendash}dynamics of random sets.},
	address = {Maui, Hawaii},
	booktitle = {51st IEEE Conference on Decision and Control},
	pages = {pp. 4751-4756}
}
@conference {
	title = {A Randomized Gossip Consensus Algorithm on Convex Metric Spaces},
	author = {Matei, Ion., Somarakis, Christoforos., Baras, John S.},
	year = {2012},
	month = {12/2012},
	abstract = {A consensus problem consists of a group of dynamic agents who seek to agree upon certain quantities of interest. This problem can be generalized in the context of convex metric spaces that extend the standard notion of convexity. In this paper we introduce and analyze a randomized gossip algorithm for solving the generalized consensus problem on convex metric spaces. We study the convergence properties of the algorithm using stochastic differential equations theory. We show that the dynamics of the distances between the states of the agents can be upper bounded by the dynamics of a stochastic differential equation driven by Poisson counters. In addition, we introduce instances of the generalized consensus algorithm for several examples of convex metric spaces.},
	address = {Maui, Hawaii},
	booktitle = {51st IEEE Conference on Decision and Control,},
	pages = {pp. 7425-7430}
}
@conference {
	title = {A comparison between upper bounds on performance of two consensus-based distributed optimization algorithms},
	author = {Matei, Ion., Baras, John S.},
	year = {2012},
	month = {09/2012},
	abstract = {In this paper we address the problem of multi-agent optimization for convex functions expressible as sums of convex functions. Each agent has access to only one function in the sum and can use only local information to update its current estimate of the optimal solution. We consider two consensus-based iterative algorithms, based on a combination between a consensus step and a subgradient decent update. The main difference between the two algorithms is the order in which the consensus-step and the subgradient descent update are performed. We obtain upper bounds on performance metrics of the two algorithms. We show that updating first the current estimate in the direction of a subgradient and then executing the consensus step ensures a tighter upper bound compared with the case where the steps are executed in reversed order. In support of our analytical results, we give some numerical simulations of the algorithms as well.},
	address = {Santa Barbara, CA},
	booktitle = {3rd IFAC Workshop on Distributed Estimation and Control in Networked Systems(NecSys 2012), pp. 168-173, Santa Barbara, California},
	pages = {pp. 168-173}
}
@conference {
	title = {Flow Control in Time-Varying, Random Supply Chain Networks},
	author = {Matei, Ion., Gueye, Assane., Baras, John S.},
	year = {2012},
	month = {07/2012},
	abstract = {Todays supply chains are more and more complex. They depend on a network of independent, yet interconnected moving parts. They rely on critical infrastructures and experience a lot of time variability and randomness.Designing strategies that deal with such constantly changing supply chains is necessary in this increasingly globalized economy where supply chain disruptions have impacts that propagate not only locally but also globally.In this paper we propose a randomized flow control algorithm for a time varying, random supply chain network. We formulate a constrained stochastic optimization problem that maximizes the profit function in terms of the long-run,time-average rates of the flows in the supply chain. We show that our algorithm, which is based on queueing theory and stochastic analysis concepts, can get arbitrarily close to the solution of the aforementioned optimization problem.},
	address = {Melbourne, Australia},
	booktitle = {20th International Symposium on Mathematical Theory of Networks and Systems (MTNS 2012)},
	pages = {pp. 1-4}
}
@conference {
	title = {Trust-Based Multi-Agent Filtering for Increased Smart Grid Security},
	author = {Matei, Ion., Baras, John S., Srinivasan, Vijay.},
	year = {2012},
	month = {07/2012},
	abstract = {We address the problem of state estimation of the power system for the Smart Grid. We assume that the monitoring of the electrical grid is done by a network of agents with both computing and communication capabilities. We propose a security mechanism aimed at protecting the state estimation process against false data injections originating from faulty equipment or cyber-attacks. Our approach is based on a multi-agent filtering scheme, where in addition to taking measurements, the agents are also computing local estimates based on their own measurements and on the estimates of the neighboring agents. We combine the multi-agent filtering scheme with a trust-based mechanism under which each agent associates a trust metric to each of its neighbors. These trust metrics are taken into account in the filtering scheme so that information transmitted from agents with low trust is disregarded. In addition, a mechanism for the trust metric update is also introduced, which ensures that agents that diverge considerably from their expected behavior have their trust values lowered.},
	address = {Barcelona, Spain},
	booktitle = {Proceedings of the 2012 Mediterranean Control Conference},
	pages = {pp. 716-721}
}
