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Abstract: The influence of node mobility on the convergence time of averaging gossipalgorithms in networks is studied. It is shown that a small number of fullymobile nodes can yield a significant decrease in convergence time. A method isdeveloped for deriving lower bounds on the convergence time by merging nodesaccording to their mobility pattern. This method is used to show that if theagents have one-dimensional mobility in the same direction the convergence timeis improved by at most a constant. Upper bounds are obtained on the convergencetime using techniques from the theory of Markov chains and show that simplemodels of mobility can dramatically accelerate gossip as long as the mobilitypaths significantly overlap. Simulations verify that different mobilitypatterns can have significantly different effects on the convergence ofdistributed algorithms.



Author: Anand D. Sarwate, Alexandros G. Dimakis

Source: https://arxiv.org/







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