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In this paper, we consider the problem of distributed average computation over communication networks whose channels are non-ideal, but noisy and/or intermittent. Channel intermittency captures randomness of network interconnections and packet-drop links. Based on input–output properties of feedback systems, we propose novel iterative algorithms that incorporate a networked feedback compensator to...
In this paper, we present a distributed algorithm to solve unconstrained distributed Least Squares optimization problems over networks where the communication links could stochastically break. The iterative algorithm allows each node with limited computation and communication capabilities to obtain the optimal solution of the centralized problem. We provide the convergence analysis of the algorithm...
In this paper, we propose a distributed dynamic consensus scheme which exhibits noise resilient property. In our setup, each node is modeled as a simple integrator. We use frequency-domain analysis to establish that the output of each node tracks the (weighted) average of the inputs of all nodes with zero steady-state error and show that a certain kind of graph Laplacain (called feasible Laplacian)...
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