The Impact of Mobility on Gossip Algorithms

We analyze how node mobility can influence the convergence time of averaging gossip algorithms on networks. Our main result is that even a small number of fully mobile nodes can yield a significant decrease in convergence time. We develop a method for deriving lower bounds on the convergence time by merging nodes according to their mobility pattern. We use this method to show that if the agents have one-dimensional mobility in the same direction the convergence time is improved by at most a constant. We also obtain upper bounds on the convergence time using techniques from the theory of Markov chains and show that simple models of mobility can dramatically accelerate gossip as long as the mobility paths significantly overlap. We use simulations to show that our bounds are still valid for more general mobility models that seem analytically intractable, and further illustrate that different mobility patterns can have significantly different effects on the convergence of distributed algorithms.