Dragana Bajovic

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We introduce a framework to study slotted Aloha with cooperative base stations. Assuming a geographic-proximity communication model, we propose several decoding algorithms with different degrees of base stations’ cooperation (non-cooperative, spatial, temporal, and spatio-temporal). With spatial cooperation, neighboring base stations inform each other(More)
We consider the problem of sensor selection for event detection in wireless sensor networks (WSNs). We want to choose a subset of <i>p</i> out of <i>n</i> sensors that yields the best detection performance. As the sensor selection optimality criteria, we propose the Kullback-Leibler and Chernoff distances between the distributions of the selected(More)
We find the exact rate for convergence in probability of products of independent, identically distributed symmetric, stochastic matrices. It is well-known that if the matrices have positive diagonals almost surely and the support graph of the mean or expected value of the random matrices is connected, the products of the matrices converge almost surely to(More)
We consider distributed optimization where N nodes in a connected network minimize the sum of their local costs subject to a common constraint set. We propose a distributed projected gradient method where each node, at each iteration k, performs an update (is active) with probability p<sub>k</sub>, and stays idle (is inactive) with probability(More)
This paper addresses robust linear dimensionality reduction (RLDR) for binary Gaussian hypothesis testing. The goal is to find a linear map from the high dimensional space where the data vector lives to a low dimensional space where the hypothesis test is carried out. The linear map is designed to maximize the detector performance. This translates into(More)
We study the asymptotic exponential decay rate I for the convergence in probability of products WkWk−1...W1 of random symmetric, stochastic matrices Wk. Albeit it is known that the probability P that the product WkWk−1...W1 is away from its limit converges exponentially fast to zero, i.e., P ∼ e−kI , the asymptotic rate I has not been computed before. In(More)
We study the large deviations performance, i.e., the exponential decay rate of the error probability, of distributed detection algorithms over random networks. At each time step k each sensor: 1) averages its decision variable with the neighbors decision variables; and 2) accounts on-the-fly for its new observation. We show that distributed detection(More)
We study, by large deviations analysis, the asymptotic performance of Gaussian running consensus distributed detection over random networks; in other words, we determine the exponential decay rate of the detection error probability. With running consensus, at each time step, each sensor averages its decision variable with the neighbors' decision variables(More)
We establish the large deviations asymptotic performance (error exponent) of consensus+innovations distributed detection over random networks with generic (non-Gaussian) sensor observations. At each time instant, sensors 1) combine theirs with the decision variables of their neighbors (consensus) and 2) assimilate their new observations (innovations). This(More)
We consider the problem of selecting a subset of p out of n sensors for the purpose of event detection, in a wireless sensor network (WSN). Occurrence or not of the event of interest is modeled as a binary Gaussian hypothesis test. In this case sensor selection consists of finding, among all (n p ) combinations, the one maximizing the Kullback-Leibler (KL)(More)