Mehmet E. Yildiz

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Motivated by applications to wireless sensor, peer-to-peer, and ad hoc networks, we study distributed broadcasting algorithms for exchanging information and computing in an arbitrarily connected network of nodes. Specifically, we study a broadcasting-based gossiping algorithm to compute the (possibly weighted) average of the initial measurements of the(More)
Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. They offer a natural tradeoff between the number of messages exchanged among terminals and the accuracy in the computation. Most of the models adopted for the message exchange in the literature, however, neither include(More)
We study binary opinion dynamics in a social network with <i>stubborn agents</i> who influence others but do not change their opinions. We focus on a generalization of the classical voter model by introducing nodes (stubborn agents) that have a fixed state. We show that the presence of stubborn agents with opposing opinions precludes convergence to(More)
In this paper we consider the problem of transmitting quantized data while performing an average consensus algorithm. Average consensus algorithms are protocols to compute the average value of all sensor measurements via near neighbors communications. The main motivation for our work is the observation that consensus algorithms offer the perfect example of(More)
This paper proposes a distributed average consensus algorithm in order to solve the cooperative spectrum sensing task without a cognitive base station. The proposed consensus algorithm converges to the optimal decision statistic in the limit. Since in practice the iteration number has to be finite, we derive high probability bounds on the iteration number(More)
Motivated by applications to wireless sensor, peerto-peer, and ad hoc networks, we have recently proposed a broadcasting-based gossiping protocol to compute the (possibly weighted) average of the initial measurements of the nodes at every node in the network. The class of broadcast gossip algorithms achieve consensus almost surely at a value that is in the(More)
In this paper we consider the problem of gossiping in a network to diffuse the average of a sub-set of nodes, called sources, and directing it to another sub-set of nodes in the network called destinations. This case generalizes the typical average consensus gossiping policy, where all nodes are both sources and destinations. We first describe prior results(More)
A crucial problem of Social Sciences is under what conditions agreement, or disagreement, emerge in a network of interacting agents. This topic has application in many contexts, including business and marketing decisions, with potential impact on information and technological networks. In this paper we consider a particular model of interaction between a(More)
Average consensus algorithms are gossiping protocols for averaging original sensor measurements via near neighbor communications. In this paper, we consider the average consensus algorithm under communication rate constraints. Without any communication rate restrictions, the algorithm ideally allows every node state to converge to the initial average in the(More)