@conference {
	title = {Trust-aware State Estimation Under False Data Injection In Distributed Sensor Networks},
	author = {Zheng, Shanshan., Jiang, Tao., Baras, John S.},
	year = {2010},
	month = {09/2010},
	abstract = {Distributed sensor networks have been widely employed to monitor and protect critical infrastructure assets. The network status can be estimated by centralized state estimation using coordinated data aggregation or by distributed state estimation, where nodes only exchange information locally to achieve enhanced scalability and adaptivity to network dynamics. One important property of state estimation is robustness against false data injection from sensors compromised by attackers. Different from most existing works in the literature that focus on centralized state estimation, we propose two novel robust distributed state estimation algorithms against false data injection. They are built upon an existing distributed Kalman filtering algorithm. In the first algorithm, we use variational Bayesian learning to estimate attack parameters and achieve performance similar to a centralized majority voting rule, without causing extra communication overhead. In the second algorithm, we introduce heterogeneity into the network by utilizing a subset of pre-trusted nodes to achieve performance better than majority voting. We show that as long as there is a path connecting each node to some of the pre-trusted nodes, the attackers can not subvert the network. Experimental results demonstrate the effectiveness of our proposed schemes.},
	address = {Austin, Texas},
	booktitle = {TECHCON 2010},
	pages = {pp. 1-4}
}
@conference {
	title = {Stochastic Average Consensus Filter for Distributed HMM Filtering: Almost Sure Convergence},
	author = {Ghasemi, Nader., Dey, Subhrakanti., Baras, John S.},
	year = {2010},
	month = {09/2010},
	abstract = {This paper studies almost sure convergence of a dynamic average consensus algorithm which allows distributed computation of the product of n time-varying conditional probability density functions. These conditional probability density functions (often called as {\textquotedblleft}belief functions{\textquotedblright}) correspond to the conditional probability of observations given the state of an underlying Markov chain, which is observed by n different nodes within a sensor network. The network topology is modeled as an undirected graph. The average consensus algorithm is used to obtain a distributed state estimation scheme for a hidden Markov model (HMM), where each sensor node computes a conditional probability estimate of the state of the Markov chain based on its own observations and the messages received from its immediate neighbors. We use the ordinary differential equation (ODE) technique to analyze the convergence of a stochastic approximation type algorithm for achieving average consensus with a constant step size. This allows each node to track the time varying average of the logarithm of conditional observation probabilities available at the individual nodes in the network. It is shown that, for a connected graph, under mild assumptions on the first and second moments of the observation probability densities and a geometric ergodicity condition on an extended Markov chain, the consensus filter state of each individual sensor converges almost surely~to the true average of the logarithm of the conditional observation probability density functions of all the sensors. Convergence is proved by using a perturbed stochastic Lyapunov function technique. Numerical results suggest that the distributed Markov chain state estimates obtained at the individual sensor nodes based on this consensus algorithm track the centralized state estimate (computed on the basis of having access to observations of all the nodes) quite well, while more formal convergence results for the distributed HMM filter to the centralized one are currently under investigation.},
	address = {Annecy, France},
	booktitle = {2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys{\textquoteright}10)},
	pages = {pp. 335-340}
}
@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}
}
