SPoT: Representing the Social, Spatial, and Temporal Dimensions of Human Mobility with a Unifying Framework

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 exible 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 exibility of the framework, and we show that SPoT 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

DTN Routing Algorithm for Networks with Nodes Social Behavior

This article presents routing algorithm in Delay and Disruptive Tolerant Networks (DTN). The main idea of this work is routing method that is based on information about nodes social behavior and their social relations in sparse structure of network. The algorithm takes advantage of friendship relationships between nodes and uses historic information to create groups of friends for each node, which is used in buffer management and forwarding phase of routing. Beside the routing method, mechanisms of collecting and exchanging of maintenance information between nodes is described. The algorithm was tested using The ONE simulation tool especially designed for DTN scenario and compared with miscellaneous popular solutions

An arrival-based framework for human mobility modeling

Modeling human mobility is crucial in the performance analysis and simulation of mobile ad hoc networks, where contacts are exploited as opportunities for peer-to-peer message forwarding. The current approach to human mobility modeling has been based on continuously modifying models, trying to embed in them the newest features of mobility properties (e.g., visiting patterns to locations or inter-contact times) as they came up from trace analysis. As a consequence, typically these models are neither flexible (i.e., features of mobility cannot be changed without changing the model) nor controllable (i.e., the exact shape of mobility properties cannot be controlled directly). In order to take into account the above requirements, in this paper we propose a mobility framework whose goal is, starting from the stochastic process describing the arrival patterns of users to locations, to generate pairwise inter-contact times and aggregate inter-contact times featuring a predictable probability distribution. We validate the proposed framework by means of simulations. In addition, assuming that the arrival process of users to locations can be described by a Bernoulli process, we mathematically derive a closed form for the pairwise and aggregate inter-contact times, proving the controllability of the proposed approach in this case