As the smartphone/cellphone market has exploded, the war on which smartphone platform will dominate has become fiercer than ever. In that vain, the goal of this paper is to answer two fundamental questions: What are the adoption trends for smartphones? And how can we estimate the demand for new smartphones? We answer these two questions by collecting a dataset of 3 million subscribers from a nationwide telecom operator. A key aspect of our work is that we have demographic information per user, such as income level, and age, which we correlate with phone usage patterns. Interestingly, we find that in all demographic groups, Android is leading platform top in all age groups and income levels. A key question is whether the “social influence” affects the choice of phone, which we find more pronounced in business plans. Finally, we develop a predictor to infer the phone a user will switch to considering: (a) the type of previous phone, (b) the social influence, and (c) the demographics of the user. Compared with the reference method, our predictor is effective in: (a) reducing the prediction error in number of phones by 1/3, and (b) in the case of minimizing phone costs, the monetary cost by half. Apart from its interest in observations, our work could help telecom operator forecast their inventory more accurately by pointing to the right properties to consider.

Wireless carriers have various churn models that are mainly based on profiling the customers and assigning churn probabilities to them. Profiling is usually limited to their individual data, such as their subscription history, demographics, usage, etc. However, our analysis of a major wireless carrier data shows that such churn prediction methods do not fully model wireless subscriber churn, and that the subscribers can be influenced by other subscribers' churn in their social network. We propose a novel method to identify `churn influencers', whose influence makes their social contacts churn subsequently. To build our model, we scored the subscribers' influence level in a way that can take current churn models into account. We further used large scale call records to identify social network and communication features that abstract the strong influencers. Using real world churn data, we trained classification tools to classify high influencers with up to ninety nine percent precision.

Many of today's applications such as cellular network management, prediction and control of the spread of biological and mobile viruses, etc., depend on the modeling and prediction of human locations. However, having widespread wireless localization technology, such as pervasive cell-tower/GPS location estimation available for only the last few years, many factors that impact human mobility patterns remain underresearched. Furthermore, many industries including telecom providers are still in need of low-cost and simple location/place prediction methods that can be implemented on a large scale. In this paper, we focus on “temporal factors” and demonstrate that they significantly impact randomness, size, and probability distribution of people's movements. We also use this information to make simple and inexpensive prediction models for subscribers' visited places. We monitored individuals for a month and divided days and hours into segments for each user to obtain probability distribution of their places for each segment of time intervals and observed major improvement in future “time-based” predictions of their location compared to when temporal factors were not considered. In addition to quantifying the improvement in place prediction, we show that significant improvements can actually be achieved through an intuitive division of time intervals with no added computational complexity.

Fine-grained dynamic population estimation is in an increasingly high demand as it has numerous applications in wireless network engineering, urban planning, location-based services and mobile applications, and advertisement. In this paper, we introduce a framework that dynamically estimates the wireless subscriber population of an arbitrary fine-grained area based on the current cellular phone usage. This framework takes advantage of strong regularities, low variance, and low information entropy in human mobility and phone usage patterns; thus simplifying the estimation for wireless carriers and other big entities while maintaining a high accuracy. We implemented our `regularity-based' framework using empirical data. Comparison with experimentally collected data shows a significant improvement in the accuracy of population estimation compared to population count based on cellular phone usage.

Location-based services enabled by broadband wireless access play an increasingly important role in people's daily navigation and coordination. Location-based applications frequently report user location to Internet servers, and location accuracy is essential to the utility of these services. However, regular and accurate location updates impact efficient usage of network resources and also users' privacy, which is not directly observed by the users. In this paper, we conducted a large scale measurement study to understand location accuracy and communication frequency of such applications. We found that while most location reports are accurate enough, some applications run in the background, reporting user locations with a higher accuracy and frequency than needed for the user's purpose. For example, while from the user perspective, hourly zip code updates are enough location information to obtain local weather conditions, some weather forecast applications report user location every five minutes or less and at GPS-level accuracy. We found that location reports from many phone applications are accurate enough and frequent enough to enable the inference of users home and work addresses, and potentially their identity, exacerbating user privacy concerns.

Recent technological advances have resulted in a dramatic change in the market shares of cellular mobile devices. However, little is known about the impact of these changes on the landscape of cellular network traffic. Using anonymized traces from one million cellular subscribers, we conduct a comparative study of the usage characteristics of three different types of mobile devices: feature phones, air cards, and smart phones. Our study covers three aspects: traffic volume in terms of data, voice, and short messages and corresponding temporal fluctuations, applications breakdown in data access, and the presence of malicious traffic. Our study reveals some similarities as well as distinct differences among the three device types. These insights into the modern cellular network traffic could influence how cellular carriers manage and provision their networks.

Social matching systems recommend people to other people. With the widespread adoption of smartphones, mobile social matching systems could potentially transform our social landscape. However, we have a limited understanding of what makes a good social match in the mobile context. We present a theoretical framework which outlines how a user's context and the rarity of different affinity measures in various contexts (match rarity) can be used to provide valuable social matches. We suggest that if a user attribute is very rare in a particular context, users will generally be more interested in an affinity match. We conducted a survey study to assess this framework with 117 respondents. We found that both context and match rarity significantly influence interest in a social match. These results validate the key aspects of the framework. We discuss the results in terms of implications for social matching system design.

In any situation where a set of personal attributes are revealed, there is a chance that revealed data can be linked back to its owner. Examples of such situations are publishing user profile micro-data or information about social ties, sharing profile information on social networking sites, or revealing personal information in computer-mediated communication (CMC). Measuring user anonymity is the first step to ensuring that the identity of the owner of revealed information cannot be inferred. Most current measures of anonymity ignore important factors such as the probabilistic nature of identity inference, the inferrer's outside knowledge, and the correlation between user attributes. Furthermore, in the social computing domain, variations in personal information and various levels of information exchange among users make the problem more complicated. We present an information-entropy-based realistic estimation of the user anonymity level to deal with these issues in social computing in an effort to help predict the identity inference risks. We then address implementation issues of online protection by proposing complexity reduction methods that take advantage of basic information entropy properties. Our analysis and delay estimation based on experimental data show that our methods are viable, effective, and efficient in facilitating privacy in social computing and synchronous CMCs.

A serious threat to user privacy in new mobile and Web2.0 applications stems from `social inferences'. These unwanted inferences are related to the users' identity, current location and other personal information. We have previously introduced `inference functions' to estimate the social inference risk based on information entropy. In this paper, after analyzing the problem and reviewing our risk estimation method, we create a decision tree to distinguish between high risk and normal situations. To evaluate our methodology, test and training datasets were collected during a large mobile-phone field study for a location-aware application. The classification tree employs our two inference functions, for the current and past situations, as internal nodes. Our results show that the achieved true classification rates are significantly better than approaches that employ other available features for the internal nodes of the trees. The results also suggest that common classification tools cannot accurately capture the information entropy for social applications. This is mostly due to the lack of enough training data for high-risk, low-entropy situations and outliers. Thus, we conclude that estimating the information entropy and the relevant inference risk using a pre-processor can yield a simpler and more accurate classification tree.

No abstract available.

Review of the literature suggests seven fundamental privacy challenges in the domain of ubiquitous social computing. To date, most research in this area has focused on the features associated with the revelation of personal location data. However, a more holistic view of privacy concerns that acknowledges these seven risks is required if we are to deploy privacy respecting next generation social computing applications. We highlight the threat associated with user inferences made possible by knowledge of the context and use of social ties. We also describe work in progress to both understand user perceptions and build a privacy sensitive urban enclave social computing system. 1.

With the growing interest in broadband wireless access (BWA) and demand for mobile high-speed connection, there is a need to extend wireless connectivity to passengers travelling in highspeed vehicles. In this paper, we use the IEEE 802.16 standard as a backhaul communication technology for broadband wireless access to railway systems. The proposed architecture uses relay elements located in the vicinity of train track to repeat the signal between the base station and the mobile vehicle. The signal transmitted from the base station is received by repeaters and relayed to the train, and vice versa. We propose spatio-temporal scheduling as a means to increase downlink throughput. The proposed spatio-temporal scheduler distributes data traffic among the repeaters in the vicinity of the train and on the route of the train. Simulation results show that a substantial improvement can be obtained when data are scheduled in both temporal and spatial dimensions.