Smartphones and the signaling messages they emit allow third parties to learn about the owners' mobility. While Wi-Fi and Bluetooth signaling messages have been (mis)used for tracking individuals, there are also privacy-respecting uses: crowd sensing for estimating the number of people in an area and their dynamics, is one such example. However, the very useful countermeasures against individual tracking, most prominently MAC address randomization, also complicate crowd size estimation. In this paper, we present an online estimation algorithm that operates only on ephemeral MAC addresses and, if desired, signal strength information to distinguish relevant signals from background noise. We use measurements and simulations to calibrate our counting algorithm and collect numerous data sets which we use to explore the algorithm's performance in different scenarios.

Accurate prediction of user mobility allows the efficient use of resources in our ubiquitously connected environment. In this work we study the predictability of the users' next location, considering a campus scenario with highly mobile users. We utilize Markov predictors, and estimate the theoretical predictability limits. Based on the mobility traces of nearly 7400 wireless network users, we estimate that the maximum predictability of the users is on average 82%, and we find that the best Markov predictor is accurate 67% of the time. In addition, we show that moderate performance gains can be achieved by leveraging multi-location prediction.

This paper presents an analysis of a large trace of user associations in a university wireless network, which includes around one thousand access points over five campuses. The trace is obtained from RADIUS authentication logs and its merit is in its recency, scale and duration. We propose a methodology for extracting association statistics from these logs, and look at visiting time distributions and processes of user arrivals to access points. We find that a large fraction of the network - around half of all access points - experiences time-varying Poisson arrival process, and association distributions can be modeled by two-stage hyper-exponential distributions at most of the access point. While network associations in campus wireless networks have been extensively studied in the literature, our study reveals changing patterns in user arrival processes and association durations, which seem to be characteristic for networks of predominantly mobile users, and allows the use of tractable network occupancy models.

In opportunistic networking, characterizing contact patterns between mobile users is essential for assessing feasibility and performance of opportunistic applications. There has been significant efforts in deriving this characterization, based on observations and trace analyses; however, most of the findings arise from studying contact opportunities at large spatial and temporal scales. Moreover, the user population is considered to be constant: no users can join or leave the system. Yet, there are many examples of scenarios which do not fully adhere to the previous assumption and cannot be accurately described at large scales. Urban environments, such as smaller city districts, are characterized by highly dynamic user populations. We believe that scenarios with varying population requires further investigation. In this paper, we present a novel modeling approach to study operation of opportunistic applications in scenarios where the population size is subjected to frequent changes, that is, it exhibits churn. We also propose an application for estimating the size of a mobile crowd, which we then use to validate our model in four scenarios: a city area, subway station, a conference and a scenario with a synthetic mobility model. We show that the model provides good representations of the investigated scenarios.

Epidemic content dissemination has been proposed as an approach to mitigate frequent link disruptions and support content-centric information dissemination in opportunistic networks. Stochastic modeling is a common method to evaluate performance of epidemic dissemination schemes. The models introduce assumptions which, on one hand make them analytically tractable, while on the other, ignore attested characteristics of human mobility. In this paper, we investigate the fitness and limitations of an analytical stochastic model for content dissemination by comparison with experimental results obtained from real mobility traces. Our finding is that a homogeneous analytic model is unable to capture the performance of content dissemination with respect to content delivery delays.

We present a demonstration scenario to validate a middleware architecture that we designed and implemented for distributing content between mobile users in an opportunistic manner. The peer-to-peer networking architecture allows content dissemination between mobile devices without relying on any infrastructure support. We developed a mobile application utilizing the services provided by the implemented middleware. The application facilitates opportunistic content distribution in both one-to-one and one-to-many dissemination modes.

The demands for ubiquitous connectivity and high data rates have led to a proliferation of mobile devices such as smartphones, media tablets and netbooks. These devices, equipped with powerful communication and storage resources, are carried by humans during their daily activities and, thus, possess a high potential for opportunistic networking. In this networking mode, encounters between users define the “topology” of the network and can be exploited for content sharing. A large portion of user encounters occur in densely populated public urban areas such as city squares, subway stations, shopping malls, etc. In this paper, we propose an analytic model to study content dissemination characteristics inside smaller areas where the mobility of nodes does not affect their connectivity. Subsequently, we argue that this model is suitable for modeling larger areas as interconnections of these basic blocks.