Localization to Enhance Security and Services in Wi-Fi Networks under Privacy Constraints

Developments of seamless mobile services are faced with two broad challenges, systems security and user privacy - access to wireless systems is highly insecure due to the lack of physical boundaries and, secondly, location based services (LBS) could be used to extract highly sensitive user information. In this paper, we describe our work on developing systems which exploit location information to enhance security and services under privacy constraints. We describe two complimentary methods which we have developed to track node location information within production University Campus Networks comprising of large numbers of users. The location data is used to enhance security and services. Specifically, we describe a method for creating geographic firewalls which allows us to restrict and enhance services to individual users within a specific containment area regardless of physical association. We also report our work on LBS development to provide visualization of spatio-temporal node distribution under privacy considerations

Mining Frequent and Periodic Association Patterns

Profiling the clients\u27 movement behaviors is useful for mobility modeling, anomaly detection, and location prediction. In this paper, we study clients\u27 frequent and periodic movement patterns in a campus wireless network. We use offline data-mining algorithms to discover patterns from clients\u27 association history, and analyze the reported patterns using statistical methods. Many of our results reflect the common characteristics of a typical academic campus, though we also observed some unusual association patterns. There are two challenges: one is to remove noise from data for efficient pattern discovery, and the other is to interpret discovered patterns. We address the first challenge using a heuristic-based approach applying domain knowledge. The second issue is harder to address because we do not have the knowledge of people\u27s activities, but nonetheless we could make reasonable interpretation of the common patterns

An empirical framework for user mobility models: Refining and modeling user registration patterns

AbstractIn this paper, we examine user registration patterns in empirical WLAN traces, identify elusive patterns that are abused as user movements in constructing empirical mobility models, and analyze them to build up a realistic user mobility model. The examination shows that about 38–90% of transitions are irrelevant to actual user movements. In order to refine the elusive movements, we investigate the geographical relationships among APs and propose a filtering framework for removing them from the trace data. We then analyze the impact of the false-positive movements on an empirical mobility model. The numerical results indicate that the proposed framework improves the fidelity of the empirical mobility model. Finally, we devise an analytical model for characterizing realistic user movements, based on the analysis on the elusive user registration patterns, which emulates elusive user registration patterns and generates true user mobile patterns

Internet Predictions

More than a dozen leading experts give their opinions on where the Internet is headed and where it will be in the next decade in terms of technology, policy, and applications. They cover topics ranging from the Internet of Things to climate change to the digital storage of the future. A summary of the articles is available in the Web extras section

Exploring Student Check-In Behavior for Improved Point-of-Interest Prediction

With the availability of vast amounts of user visitation history on location-based social networks (LBSN), the problem of Point-of-Interest (POI) prediction has been extensively studied. However, much of the research has been conducted solely on voluntary checkin datasets collected from social apps such as Foursquare or Yelp. While these data contain rich information about recreational activities (e.g., restaurants, nightlife, and entertainment), information about more prosaic aspects of people's lives is sparse. This not only limits our understanding of users' daily routines, but more importantly the modeling assumptions developed based on characteristics of recreation-based data may not be suitable for richer check-in data. In this work, we present an analysis of education "check-in" data using WiFi access logs collected at Purdue University. We propose a heterogeneous graph-based method to encode the correlations between users, POIs, and activities, and then jointly learn embeddings for the vertices. We evaluate our method compared to previous state-of-the-art POI prediction methods, and show that the assumptions made by previous methods significantly degrade performance on our data with dense(r) activity signals. We also show how our learned embeddings could be used to identify similar students (e.g., for friend suggestions).Comment: published in KDD'1

Location and routing optimization protocols supporting internet host mobility

PhD ThesisWith the popularity of portable computers and the proliferation of wireless networking interfaces, there is currently a great deal of interest in providing IP networking support for host mobility using the Internet as a foundation for wireless networking. Most proposed solutions depend on a default route through the mobile host's horne address, which makes for unnecessarily long routes. The major problem that this gives rise to is that of finding an efficient way of locating and routing that allows datagrams to be delivered efficiently to moving destinations whilst limiting costly Internet-wide location updates as much as possible. Two concepts - "local region" and "patron service" - are introduced based on the locality features of the host movement and packet traffic patterns. For each mobile host, the local region is a set of designated subnetworks within which a mobile host often moves, and the patrons are the hosts from which the majority of traffic for the mobile host originated. By making use of the hierarchical addressing and routing structure of Internet, the two concepts are used to confine the effects of a host moving, so location updates are sent only to a designated host moving area and to those hosts which are most likely to call again, thus providing nearly optimal routing for most communication. The proposed scheme was implemented as an IP extension using a network simulator and evaluated from a system performance point of view. The results show a significant reduction in the accumulated communication time along with improved datagram tunneling, as compared with its extra location overhead. In addition, a comparison with another scheme shows that our functionality is more effective both for location update and routing efficiency. The scheme offers improved network and host scalability by isolating local movement from the rest of the world, and provides a convenient point at which to perform administration functions