SPoT: Representing the social, spatial, and temporal dimensions of human mobility with a unifying framework

Abstract

Modeling human mobility is crucial in the analysis and simulation of opportunistic networks, where contacts are exploited as opportunities for peer-topeer message forwarding. The current approach with human mobility modeling has been based on continuously modifying models, trying to embed in them the mobility properties (e.g., visiting patterns to locations or specific distributions of inter-contact times) as they came up from trace analysis. As a consequence, with these models it is difficult, if not impossible, to modify the features of mobility or to control the exact shape of mobility metrics (e.g., modifying the distribution of inter-contact times). For these reasons, in this paper we propose a mobility framework rather than a mobility model, with the explicit goal of providing a flexible and controllable tool for modeling mathematically and generating simulatively different possible features of human mobility.Our framework, named SPoT, is able to incorporate the three dimensions -spatial, social, and temporal -of human mobility. The way SPoT does it is by mapping the different social communities of the network into different locations, whose members visit with a configurable temporal pattern. In order to characterize the temporal patterns of user visits to locations and the relative positioning of locations based on their shared users, we analyze the traces of real user movements extracted from three location-based online social networks (Gowalla, Foursquare, and Altergeo). We observe that a Bernoulli process effectively approximates user visits to locations in the majority of cases and that locations that share many common users visiting them frequently tend to be located close to each other. In addition, we use these traces to test the flexibility of the framework, and we show that SPoT Preprint submitted to Elsevier July 16, 2013 is able to accurately reproduce the mobility behavior observed in traces. Finally, relying on the Bernoulli assumption for arrival processes, we provide a throughout mathematical analysis of the controllability of the framework, deriving the conditions under which heavy-tailed and exponentially-tailed aggregate inter-contact times (often observed in real traces) emerge.

Human mobility in opportunistic networks: Characteristics, models and prediction methods

“…Then, the spatial and temporal dimensions of mobility are added. × × √ × × -√ √ √ √ × √ Social, sPatial, and Temporal mobility framework (SPoT) (Karamshuk, Boldrini, 2013) considers the three main aspects of mobility to provide a flexible and controllable mobility framework rather than a model. SPoT aims to generate different mobility models by modifying mobility properties (e.g., ICTs) simultaneously.…”

T-START: Time, Status and Region Aware Taxi Mobility Model for Metropolis

“…The model will be distributed in multiple communities in different regions according to the degree of closeness between nodes. Also, Social, sPatial, and Temporal mobility framework (SPoT) takes a social graph as input and the spatial and temporal dimensions of mobility are added [32].…”

Routing for context-addressable messages using Predefined Concept Connection Map in delay tolerant network

“…An example of this is the work based on the collection of predefined realistic locations as in Hsu et al, 25 or meetings at certain times resulting from the schedule of human activity, 26,27 sometimes extending this in the context of human social behavior. 28 The advanced modelling approaches which merge the complex properties of human mobility and sociality 29,30 were made based on this assumption. All these solutions are related to the proposed CBR-DTN because of nodes behaviour modelling.…”