Our goal is to explore characteristics of the wireless environment that provide opportunities for caching, prefetching, coverage planning, and resource reservation. We conducted a one-month measurement study of locality phenomena among wireless web users and their association patterns on a major university campus using the IEEE 802.11 wireless infrastructure.

Wearable computers have the potential to act as intelligent agents in everyday life and assist the user in a variety of tasks depending on the context. Location is the most common form of context used by these agents to determine the user's task. However, another potential use is the creation of a predictive model of the user's future movements. We present a system that automatically clusters GPS data taken over an extended period of time into meaningful locations at multiple scales. These locations are then incorporated into a Markov model that can be consulted for use with a variety of applications in both single--user and collaborative scenarios.

This article presents a survey on location managemental orithms for next-generation personal communications networks. We first describe dif f erent static and dynamic location update algorithms. Then we discuss various selective paging strategies. We also present various modeling techniques that have been used for the performance analysis of location update and terminal pagin. We conclude by stating a number of open problems that need to be add r e d for the deployment of next-generation PCNs. ver the past few years, there has becn tremen-dous growth in wireless communications. Pcr-sonal communications service (PCS) subscribcrs are increasing at an exponential rate and will cont?nue to increase in the near future. The next-generation personal communications network (PCN) is being standard-ized as part of the International Mobile Telccommunications

Abstract. The complexity of the mobility tracking problem in a cellular environment has been characterized under an information-theoretic framework. Shannon’s entropy measure is identified as a basis for comparing user mobility models. By building and maintaining a dictionary of individual user’s path updates (as opposed to the widely used location updates), the proposed adaptive on-line algorithm can learn subscribers ’ profiles. This technique evolves out of the concepts of lossless compression. The compressibility of the variable-to-fixed length encoding of the acclaimed Lempel–Ziv family of algorithms reduces the update cost, whereas their built-in predictive power can be effectively used to reduce paging cost.

Abstract — This paper presents a mobile tracking scheme that exploits the predictability of user mobility patterns in wireless PCS networks. In this scheme, a mobile’s future location is predicted by the network, based on the information gathered from the mobile’s recent report of location and velocity. When a call is made, the network pages the destination mobile around the predicted location. A mobile makes the same location prediction as the network does; it inspects its own location periodically and reports the new location when the distance between the predicted and the actual locations exceeds a threshold. To more realistically represent the various degrees of velocity correlation in time, a Gauss-Markov mobility model is used. For practical systems where the mobility pattern varies over time, we propose a dynamic Gauss-Markov parameter estimator that provides the mobility parameters to the prediction algorithm. Based on the Gauss-Markov model, we describe an analytical framework to evaluate the cost of mobility management for the proposed scheme. We also present an approximation method that reduces the computational complexity of the cost evaluation for multi-dimensional systems. We then compare the cost of predictive mobility management against that of the regular, nonpredictive distance-based scheme, for both the case with ideal Gauss-Markov mobility pattern and the case with time-varying mobility pattern. The performance advantage of the proposed scheme is demonstrated under various mobility patterns, call patterns, location inspection cost, location updating cost, mobile paging cost, and frequencies of mobile location inspections. As a point of reference, prediction can reduce the mobility management cost by more than 50 % for all systems, where a the mobile users have moderate mean velocity and where performing a single location update is as least as expensive as paging a mobile in one cell. Index Terms — predictive mobility management, Gauss-Markov model, distance-based location management, mobility pattern, random walk, fluid flow, dynamic parameter estimation, wireless networking I.

Abstract. A datatype with increasing importance in GIS is what we call the location history–a record of an entity’s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories. 1

The study of complex social systems has traditionally been an arduous process, involving extensive surveys, interviews, ethnographic studies, or analysis of online behavior. Today, however, it is possible to use the unprecedented amount of information generated by pervasive mobile phones to provide insights into the dynamics of both individual and group behavior. Information such as continuous proximity, location, communication and activity data, has been gathered from the phones of 100 human subjects at MIT. Systematic measurements from these 100 people over the course of eight months have generated one of the largest datasets of continuous human behavior ever collected, representing over 300,000 hours of daily activity. In this thesis we describe how this data can be used to uncover regular rules and structure in behavior of both individuals and organizations, infer relationships between subjects, verify selfreport

Global wireless networks enable mobile users to communicate regardless of their locations. One of the most important issues is location management in a highly dynamic environment because mobile users may roam between different wireless systems, network operators, and geographical regions. In this paper, a location-tracking mechanism is introduced that consists of intersystem location updates and intersystem paging. Intersystem update is implemented by using the concept of boundary location area, which is determined by a dynamic location update policy in which the velocity and the quality of service are taken into account on a per-user basis. Also, intersystem paging is based on the concept of boundary location register, which is used to maintain the records of mobile users crossing the boundary of systems. This mechanism not only reduces location-tracking costs, but also significantly decreases call-loss rates and average-paging delays. The performance evaluation of the proposed schemes is provided to demonstrate their effectiveness in multitier personal communication systems.

As mobile devices continue to shrink, users are no longer merely nomads, but truly mobile, employing devices on the move. At the same time, these users no longer rely on a single managed network, but exploit a wide variety of connectivity options as they spend their day. Together, these trends argue that systems must consider the derivative of connectivity— the changes inherent in movement between separately managed networks, with widely varying capabilities. To manage the derivative of connectivity, we exploit the fact that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device’s owner, and customizes a predictive mobility model for that specific user. Rather than rely on a synthetic model or aggregate observations, this custom-tailored model can be used together with past observations of wireless network capabilities to generate connectivity forecasts. Applications can in turn use these forecasts to plan future network use with confidence. We have built a BreadCrumbs prototype, and evaluated it with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications. 1

Abstract The goal of the MavHome (Managing An Intelligent Versatile Home) project is to create a home that acts as a rational agent. The agent seeks to maximize inhabitant comfort and minimize operation cost. In order to achieve these goals, the agent must be able to predict the mobility patterns and device usages of the inhabitants. In this paper, we introduce the MavHome project and its underlying architecture. The role of prediction algorithms within the architecture is discussed, and three prediction algorithms which are central to the home operations are presented. We demonstrate the effectiveness of these algorithms on synthetic and/or actual smart home data.