Periodic Properties of User Mobility and Access-Point Popularity

Understanding user mobility and its effect on access points (APs) is important in designing location-aware systems and wireless networks. Although various studies of wireless networks have provided useful insights, it is hard to apply them to other situations. Here we present a general methodology for extracting mobility information from wireless network traces, and for classifying mobile users and APs. We used the Fourier transform to reveal important periods and chose the two strongest periods to serve as parameters to a classification system based on Bayes\u27 theory. Analysis of 1-month traces shows that while a daily pattern is common among both users and APs, a weekly pattern is common only for APs. Analysis of 1-year traces revealed that both user mobility and AP popularity depend on the academic calendar. By plotting the classes of APs on our campus map, we discovered that their periodic behavior depends on their proximity to other APs

Identifying Unusual Days

Pervasive applications such as digital memories or patient monitors collect a vast amount of data. One key challenge in these systems is how to extract interesting or unusual information. Because users cannot anticipate their future interests in the data when the data is stored, it is hard to provide appropriate indexes. As location-tracking technologies, such as global positioning system, have become ubiquitous, digital cameras or other pervasive systems record location information along with the data. In this paper, we present an automatic approach to identify unusual data using location information. Given the location information, our system identifies unusual days, that is, days with unusual mobility patterns. We evaluated our detection system using a real wireless trace, collected at wireless access points, and demonstrated its capabilities. Using our system, we were able to identify days when mobility patterns changed and differentiate days when a user followed a regular pattern from the rest. We also discovered general mobility characteristics. For example, most users had one or more repeating mobility patterns, and repeating mobility patterns did not depend on certain days of the week, except that weekends were different from weekdays

Extracting a Mobility Model from Real User Traces

Understanding user mobility is critical for simulations of mobile devices in a wireless network, but current mobility models often do not reflect real user movements. In this paper, we provide a foundation for such work by exploring mobility characteristics in traces of mobile users. We present a method to estimate the physical location of users from a large trace of mobile devices associating with access points in a wireless network. Using this method, we extracted tracks of always-on Wi-Fi devices from a 13-month trace. We discovered that the speed and pause time each follow a log-normal distribution and that the direction of movements closely reflects the direction of roads and walkways. Based on the extracted mobility characteristics, we developed a mobility model, focusing on movements among popular regions. Our validation shows that synthetic tracks match real tracks with a median relative error of 17%

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

Abstract Modeling users ’ mobility among WiFi access points

Modeling movements of users is important for simulating wireless networks, but current models often do not reflect real movements. Using real mobility traces, we can build a mobility model that reflects reality. In building a mobility model, it is important to note that while the number of handheld wireless devices is constantly increasing, laptops are still the majority in most cases. As a laptop is often disconnected from the network while a user is moving, it is not feasible to extract the exact path of the user from network messages. Thus, instead of modeling individual user’s movements, we model movements in terms of the influx and outflux of users between access points (APs). We first counted the hourly visits to APs in the syslog messages recorded at APs. We found that the number of hourly visits has a periodic repetition of 24 hours. Based on this observation, we aggregated multiple days into a single day by adding the number of visits of the same hour in different days. We then clustered APs based on the different peak hour of visits. We found that this approach of clustering is effective; we ended up with four distinct clusters and a cluster of stable APs. We then computed the average arrival rate and the distribution of the daily arrivals for each cluster. Using a standard method (such as thinning) for generating nonhomogeneous Poisson processes, synthetic traces can be generated from our model.

Providing safety and visibility for mobile users.

Mobile users bring new challenges to distributed file systems. First, the network costs between clients and servers vary due to mobility of clients. Second, mobile clients are less available due to the absence of network connectivity or being suspended for power savings. Third, mobile clients are unreliable because they are highly susceptible to breakage and theft. Current file systems needlessly combine safety and visibility; a client propagates the contents of an update, implicitly notifying the server that the update exists. Fluid Replication separates them through the addition intermediate servers, called WayStations. While traveling, a client associates itself with a nearby WayStation that provides replication services. Updates are sent to the nearby WayStation for safety, while WayStations and servers frequently exchange knowledge of updates through reconciliation to provide visibility. In this way, Fluid Replication provides safety and visibility to file updates over the wide-area with performance comparable to the local-area. To choose a nearby WayStation, clients need to know the available network capacity to WayStations. This is particularly difficult in mobile networks where capacity changes frequently. Current systems depend on static exponentially weighted moving average (EWMA) filters. These filters are either able to detect true changes quickly or to mask transients, but cannot do both. This motivated the design of a new network filter. This filter is agile when possible and stable when necessary; it adapts to the prevailing network conditions. During reconciliation, a WayStation sends the server update notifications without the contents of those updates. While sending notifications frequently is important to improve consistency, sending data should be deferred to reduce server load. However, deferring of data shipment penalizes clients who share because to serve these clients, the server must fetch updated files from other WayStations, called back fetch, over a wide-area network. To solve this problem, a heuristic that predicts future file sharing has been developed. It is based on the observation that past instances of sharing are likely to lead to future ones. It reduces data shipped by orders of magnitude compared to aggressive schemes, while reducing the number of back-fetches by nearly half compared to on-demand shipment.Ph.D.Applied SciencesComputer scienceElectrical engineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/124458/2/3138200.pd

Modeling users’ mobility among WiFi access points

Modeling movements of users is important for simulating wireless networks, but current models often do not reflect real movements. Using real mobility traces, we can build a mobility model that reflects reality. In building a mobility model, it is important to note that while the number of handheld wireless devices is constantly increasing, laptops are still the majority in most cases. As a laptop is often disconnected from the network while a user is moving, it is not feasible to extract the exact path of the user from network messages. Thus, instead of modeling individual user’s movements, we model movements in terms of the influx and outflux of users between access points (APs). We first counted the hourly visits to APs in the syslog messages recorded at APs. We found that the number of hourly visits has a periodic repetition of 24 hours. Based on this observation, we aggregated multiple days into a single day by adding the number of visits of the same hour in different days. We then clustered APs based on the different peak hour of visits. We found that this approach of clustering is effective; we ended up with four distinct clusters and a cluster of stable APs. We then computed the average arrival rate and the distribution of the daily arrivals for each cluster. Using a standard method (such as thinning) for generating nonhomogeneous Poisson processes, synthetic traces can be generated from our model.