Souk : Spatial Observation of Human Kinetics

Abstract

Simulating human-centered pervasive systems requires accurate assumptions on the behavior of human groups. Recent models consider this behavior as a combination of both social and spatial factors. Yet, establishing accurate traces of human groups is difficult: current techniques capture either positions, or contacts, with a limited accuracy.In this paper we introduce a new technique to capture such behaviors. The interest of this approach lies in the unprecedented accuracy at which both positions and orientations of humans, even gathered in a crowd, are captured. The open-source software pipeline we developed to exploit captured data allows extraction of several metrics on movement and social contacts, and permits study of their respective interrelationship. From the mobility to the topological connectivity, this framework offers a layered approach that can be tailored, allowing to compare and reason about models and traces.We demonstrate the accuracy and validity of our approach on social events and calibration runs in which we captured the motions of humans. In particular, we introduce an open-access trace of 50 individuals and compare it against random waypoint models that have the same global characteristics. Our fine-grain analyses, that take into account social interactions between users, show that the random way point model does not provide accurate predictions for socially-induced motion; to model human kinetics, new group-and interaction-based models should be developed. From the computer science point of view, these models are required to fully exploit the power of human-centered mobile computing, crucial for ubiquitous computing, and referred to as Short Range Communication Systems, Mobile Opportunistic Networking, or Mobile Networking in Proximity.

Modeling and measuring graph similarity

“…The first case study is based on the SOUK dataset [9]. This dataset captures the social interactions of 45 individuals during a cocktail, see [9] for more details.…”

“…The first case study is based on the SOUK dataset [9]. This dataset captures the social interactions of 45 individuals during a cocktail, see [9] for more details. The dataset consists in 300 discrete timesteps, describing the dynamic interaction graph between the participants, one timesteps every 3 seconds.…”

The many faces of graph dynamics

“…• Souk (SK): This dataset captures the social interactions of 45 individuals during a cocktail, see [9] for more details. The dataset consists of 300 snapshots, describing the dynamic interaction graph between the participants, one time step every 3 seconds [9]. Figure 2 provides a temporal overview on the evolution of the number of edges in the network and the GED between consecutive snapshots.…”

Does Mobility Matter? An Evaluation Methodology for Opportunistic Apps

“…For example, the authors of [14] and [15] proposed social mobility models capturing the interactions between mobile users. Furthermore, Killijian et al [16] collected the real trace data from a crowded environment that can be used to develop a heuristic to extract mobility characteristics from mobility tracks. Nevertheless, the more complex the model is, the more difficult the parameters setup.…”

“…Unfortunately, the generation of representative real-world traces is not trivial. It requires the collaboration of tens or hundreds of volunteers and facilities to track their position with accuracy [16]. This fact makes that real-world traces are typically difficult to obtain.…”