SLAW: Self-Similar Least-Action Human Walk

Abstract

Many empirical studies of human walks have reported that there exist fundamental statistical features commonly appearing in mobility traces taken in various mobility settings. These include: 1) heavy-tail flight and pause-time distributions; 2) heterogeneously bounded mobility areas of individuals; and 3) truncated power-law intercontact times. This paper reports two additional such features: a) The destinations of people (or we say waypoints) are dispersed in a self-similar manner; and b) people are more likely to choose a destination closer to its current waypoint. These features are known to be influential to the performance of human-assisted mobility networks. The main contribution of this paper is to present a mobility model called Self-similar Least-Action Walk (SLAW) that can produce synthetic mobility traces containing all the five statistical features in various mobility settings including user-created virtual ones for which no empirical information is available. Creating synthetic traces for virtual environments is important for the performance evaluation of mobile networks as network designers test their networks in many diverse network settings. A performance study of mobile routing protocols on top of synthetic traces created by SLAW shows that SLAW brings out the unique performance features of various routing protocols.