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

Evaluating Mobility Predictors in Wireless Networks for Improving Handoff and Opportunistic Routing

We evaluate mobility predictors in wireless networks. Handoff prediction in wireless networks has long been considered as a mechanism to improve the quality of service provided to mobile wireless users. Most prior studies, however, were based on theoretical analysis, simulation with synthetic mobility models, or small wireless network traces. We study the effect of mobility prediction for a large realistic wireless situation. We tackle the problem by using traces collected from a large production wireless network to evaluate several major families of handoff-location prediction techniques, a set of handoff-time predictors, and a predictor that jointly predicts handoff location and time. We also propose a fallback mechanism, which uses a lower-order predictor whenever a higher-order predictor fails to predict. We found that low-order Markov predictors, with our proposed fallback mechanisms, performed as well or better than the more complex and more space-consuming compression-based handoff-location predictors. Although our handoff-time predictor had modest prediction accuracy, in the context of mobile voice applications we found that bandwidth reservation strategies can benefit from the combined location and time handoff predictor, significantly reducing the call-drop rate without significantly increasing the call-block rate. We also developed a prediction-based routing protocol for mobile opportunistic networks. We evaluated and compared our protocol\u27s performance to five existing routing protocols, using simulations driven by real mobility traces. We found that the basic routing protocols are not practical for large-scale opportunistic networks. Prediction-based routing protocols trade off the message delivery ratio against resource usage and performed well and comparable to each other

Classifying the mobility of users and the popularity of access points

Abstract. There is increasing interest in location-aware systems and applications. It is important for any designer of such systems and applications to understand the nature of user and device mobility. Furthermore, an understanding of the effect of user mobility on access points (APs) is also important for designing, deploying, and managing wireless networks. Although various studies of wireless networks have provided insights into different network environments and user groups, it is often hard to apply these findings to other situations, or to derive useful abstract models. In this paper, 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 convert time-dependent location information to the frequency domain, then chose the two strongest periods and used them as parameters to a classification system based on Bayesian theory. To classify mobile users, we computed diameter (the maximum distance between any two APs visited by a user during a fixed time period) and observed how this quantity changes or repeat