Improved Bounds on Information Dissemination by Manhattan Random Waypoint Model

TL;DR

This paper analyzes the speed of information spread among mobile agents in a grid using the Manhattan Random Waypoint model, providing new bounds and validating predictions with real-world taxi data.

Contribution

It introduces improved theoretical bounds on flood time for the Manhattan Random Waypoint model and validates these bounds with simulations and real-world data.

Findings

Flood time is bounded by O(N log M (N/M) log(NM)) with high probability.

The model accurately predicts information dissemination times in real-world taxi trajectory data.

Simulations confirm the theoretical bounds across different network configurations.

Abstract

With the popularity of portable wireless devices it is important to model and predict how information or contagions spread by natural human mobility -- for understanding the spreading of deadly infectious diseases and for improving delay tolerant communication schemes. Formally, we model this problem by considering $M$ moving agents, where each agent initially carries a \emph{distinct} bit of information. When two agents are at the same location or in close proximity to one another, they share all their information with each other. We would like to know the time it takes until all bits of information reach all agents, called the \textit{flood time}, and how it depends on the way agents move, the size and shape of the network and the number of agents moving in the network. We provide rigorous analysis for the \MRWP model (which takes paths with minimum number of turns), a convenient model used previously to analyze mobile agents, and find that with high probability the flood time is bounded by $O\big(N\log M\lceil(N/M) \log(NM)\rceil\big)$, where $M$ agents move on an $N\times N$ grid. In addition to extensive simulations, we use a data set of taxi trajectories to show that our method can successfully predict flood times in both experimental settings and the real world.