Blowtooth: pervasive gaming in unique and challenging environments

This paper describes Blowtooth, a Bluetooth implemented pervasive game where players smuggle virtual drugs through real airport security with the help of unknowing bystanders. The game explores the nature of pervasive game playing in environments that are not generally regarded as playful or “fun,” and where people are subject to particularly high levels of intrusive surveillance and monitoring. Six participants who were travelling internationally within a two-week period were recruited to evaluate the game. Findings suggest that creating pervasive games that incorporate the unique features of their context as part of the game may provide enjoyable, novel and thought-provoking experiences for players

Blowtooth: a provocative pervasive game for smuggling virtual drugs through real airport security

In this paper we describe a pervasive game, Blowtooth, in which players use their mobile phones to hide virtual drugs on nearby airline passengers in real airport check-in queues. After passing through airport security, the player must find and recover their drugs from the innocent bystanders, without them ever realizing they were involved in the game. The game explores the nature of pervasive game playing in environments that are not, generally, regarded as playful or “fun”. This paper describes the game’s design and implementation as well as an evaluation conducted with participants in real airports. It explores the players’ reactions to the game through questionnaire responses and in-game activity. The technologies used in Blowtooth are, intentionally, simple in order for the enjoyment of the game to be reliant more on the physical environment rather than the enabling technologies. We conclude that situating pervasive games in unexpected and challenging environments, such as international airports, may provide interesting and unique gaming experiences for players. In addition, we argue that pervasive games benefit most from using the specific features and nature of interesting real-world environments rather than focusing on the enabling technologies

On the Dynamics of Human Proximity for Data Diffusion in Ad-Hoc Networks

We report on a data-driven investigation aimed at understanding the dynamics of message spreading in a real-world dynamical network of human proximity. We use data collected by means of a proximity-sensing network of wearable sensors that we deployed at three different social gatherings, simultaneously involving several hundred individuals. We simulate a message spreading process over the recorded proximity network, focusing on both the topological and the temporal properties. We show that by using an appropriate technique to deal with the temporal heterogeneity of proximity events, a universal statistical pattern emerges for the delivery times of messages, robust across all the data sets. Our results are useful to set constraints for generic processes of data dissemination, as well as to validate established models of human mobility and proximity that are frequently used to simulate realistic behaviors.Comment: A. Panisson et al., On the dynamics of human proximity for data diffusion in ad-hoc networks, Ad Hoc Netw. (2011

Distributed Community Detection in Dynamic Graphs

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} \sdG(n,p,q) where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=\Theta(1/n)$).Comment: Version I

Modern temporal network theory: A colloquium

The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.Comment: Final accepted versio

Human dynamic networks in opportunistic routing and epidemiology

Measuring human behavioral patterns has broad application across different sciences. An individual’s social, proximal and geographical contact patterns can have significant importance in Delay Tolerant Networking (DTN) and epidemiological modeling. Recent advances in computer science have not only provided the opportunity to record these behaviors with considerably higher temporal resolution and phenomenological accuracy, but also made it possible to record specific aspects of the behaviors which have been previously difficult to measure. This thesis presents a data collection system using tiny sensors which is capable of recording humans’ proximal contacts and their visiting pattern to a set of geographical locations. The system also collects information on participants’ health status using weekly surveys. The system is tested on a population of 36 participants and 11 high-traffic public places. The resulting dataset offers rich information on human proximal and geographic contact patterns cross-linked with their health information. In addition to the basic analysis of the dataset, the collected data is applied to two different applications. In DTNs the dataset is used to study the importance of public places as relay nodes, and described an algorithm that takes advantage of stationary nodes to improve routing performance and load balancing in the network. In epidemiological modeling, the collected dataset is combined with data on H1N1 infection spread over the same time period and designed a model on H1N1 pathogen transmission based on these data. Using the collected high-resolution contact data as the model’s contact patterns, this work represents the importance of contact density in addition to contact diversity in infection transmission rate. It also shows that the network measurements which are tied to contact duration are more representative of the relation between centrality of a person and their chance of contracting the infection

Measuring and designing social mechanisms using mobile phones

Thesis (Ph. D.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 157-168).A key challenge of data-driven social science is the gathering of high quality multi-dimensional datasets. A second challenge relates to the design and execution of social experiments in the real world that are as reliable as those within a controlled laboratory, yet yield more practical results. We introduce the Social Functional Mechanism-design and Relationship Imaging, or "SocialfMRI" - an approach that enhances existing computational social science methodologies by bridging rich data collection strategies with experimental interventions. In this thesis, we demonstrate the value of the Social fMRI approach in our Friends and Family study. We transformed a young-family residential community into a living laboratory for 15 months, through a very fine-grained and longitudinal data collection process combined with targeted experimental interventions. Through the derived dataset of unprecedented quality, the Social fMRI approach allows us to gain insights into intricate social mechanisms and interpersonal relationships within the community in ways not previously possible. This thesis delivers the following contributions: (1) A methodology combining a rich-data experimental approach together with carefully designed interventions, (2) a system supporting the methodology - implemented, field-tested, and released to the world as an open-source framework with a growing community of users, (3) a dataset collected using the system, comprising what is, to date, the richest real-world dataset of its genre, (4) a very large set of experimental findings that contribute to our understanding of important research questions in computational social science in addition to demonstrating the methodology's potential. Among the results described in this thesis are the design and evaluation of a novel mechanism for social support in a health-related context, the observation that the diffusion of mobile applications relies more on the face-to-face interaction ties than on self-perceived friendship ties, and a gained understanding of the evolution of modeling and prediction processes over time and varying sample sizes.by Nadav Aharony.Ph.D