The Impact of Mobility on Gossip Algorithms

TL;DR

This paper investigates how node mobility affects the speed of gossip algorithms, showing that mobility can significantly accelerate convergence depending on the mobility pattern and overlap.

Contribution

It introduces a method to derive lower bounds on convergence time based on mobility patterns and demonstrates how mobility can dramatically improve gossip algorithm performance.

Findings

Small number of mobile nodes can greatly reduce convergence time

Mobility with overlapping paths accelerates gossip algorithms

Different mobility patterns have varying impacts on convergence speed

Abstract

The influence of node mobility on the convergence time of averaging gossip algorithms in networks is studied. It is shown that a small number of fully mobile nodes can yield a significant decrease in convergence time. A method is developed for deriving lower bounds on the convergence time by merging nodes according to their mobility pattern. This method is used to show that if the agents have one-dimensional mobility in the same direction the convergence time is improved by at most a constant. Upper bounds are obtained 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. Simulations verify that different mobility patterns can have significantly different effects on the convergence of distributed algorithms.