Homesick L\'evy walk: A mobility model having Ichi-go Ichi-e and scale-free properties of human encounters

TL;DR

This paper introduces the Homesick Lévy walk, a new mobility model that explains the scale-free and Ichi-go Ichi-e encounter patterns observed in human contact data, aiding the evaluation of delay tolerant networks.

Contribution

The paper proposes the Homesick Lévy walk as a minimal human mobility model that captures key encounter statistics and provides a theoretical basis for these properties.

Findings

Most human encounters are one-time (Ichi-go Ichi-e).

Remaining encounters follow a power-law distribution.

The model's simulations and theory match observed encounter patterns.

Abstract

In recent years, mobility models have been reconsidered based on findings by analyzing some big datasets collected by GPS sensors, cellphone call records, and Geotagging. To understand the fundamental statistical properties of the frequency of serendipitous human encounters, we conducted experiments to collect long-term data on human contact using short-range wireless communication devices which many people frequently carry in daily life. By analyzing the data we showed that the majority of human encounters occur once-in-an-experimental-period: they are Ichi-go Ichi-e. We also found that the remaining more frequent encounters obey a power-law distribution: they are scale-free. To theoretically find the origin of these properties, we introduced as a minimal human mobility model, Homesick L\'evy walk, where the walker stochastically selects moving long distances as well as L\'evy walk or returning back home. Using numerical simulations and a simple mean-field theory, we offer a theoretical explanation for the properties to validate the mobility model. The proposed model is helpful for evaluating long-term performance of routing protocols in delay tolerant networks and mobile opportunistic networks better since some utility-based protocols select nodes with frequent encounters for message transfer.