[Show abstract][Hide abstract] ABSTRACT: Locator@CMU is a centralized wireless location service that provides information on all connected 802.11 devices on Carnegie Mellon's Wireless Andrew network. The basic architecture and functionality of this service are presented. In addition the implementation of a location-based application is discussed. The results are presented in the context of a new location-based service that allows for the determination of the number of clients at an access point at a specified future time. The access points are classified into four categories and results presented based on each type. Our results show that using the Locator@CMU system can predict future access point utilization to 30% accuracy. This work presents the potential foundation for the creation of a system that can be used to .provide this information for interested social or dynamic network configuration programs.
[Show abstract][Hide abstract] ABSTRACT: SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user's context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists.
[Show abstract][Hide abstract] ABSTRACT: This paper describes a system developed for determining locations of devices on 802.11 wireless networks. Data representing 18,000 computers registered on Carnegie Mellon's Wireless Andrew is presented from traces taken in 2003 and 2005. Developers make many assumptions when creating applications for wearable and pervasive computers. The data collected by Locator@CMU provides a clearer understanding of large-scale wireless networks and their usage for implementing services and programs. Among the findings, we examine the mobility of a user as defined by percentage of time spent at their home site and favorite sites. Our results show that only a small number of wireless users exhibit high mobility and our data suggests that typical mobile users utilize the network only in a handful of sites. These basic patterns have remained steady over the past two years.
[Show abstract][Hide abstract] ABSTRACT: The ability to determine the location of a mobile device is a challenge that has persistently evaded technologists. Although
solutions to this problem have been extensively developed, none provide the accuracy, range, or cost-effectiveness to serve
as a solution over a large urban area. The Global Positioning System (GPS) does not work well indoors or in urban environments.
Infrared based systems require line-of-site, are costly to install and do not perform well in direct sunlight . Cellular
network-based positioning systems are limited by cell size and also do not work well indoors . The list goes on. With
the rise of Wireless Internet, or WiFi as it is commonly dubbed, the best infrastructure for location awareness to date has
been created. WiFi is standardized, inexpensive to deploy, easy to install and a default component in a wide-range of consumer
devices. These characteristics are the drivers behind WiFi’s most significant trait: increasing ubiquity. By developing within
the existing 802.11 infrastructure, developers can leverage WiFi to create wide-spread context-aware services.