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Advances in wireless technologies in the last ten years have created considerable opportunities as well as challenges for wireless body medical systems. The foremost challenge is how to build a reliable system connecting heterogeneous body sensors and actuators in an open system environment. In this dissertation, we present our work towards this goal. The system addresses four design issues: the underlying network architecture, the network scheduling disciplines, the location determination and tracking methods, and the embedded application execution architecture. We first present the design of an adaptive wireless protocol (MBStarPlus) to provide the basic wireless platform WBAN (Wireless Body Area Network). MBStarPlus is a real-time, secure, robust and flexible wireless network architecture. It is designed to utilize any low-power wireless radio as its physical layer. The TDMA mechanism is adopted for realtime data delivery. The time-slot length is adjustable for flexibility. Multiple technologies are utilized to provide reliability and security. We next investigate how to coordinate the body sensors/actuators that can optimally select from a range (maximum and minimum) of data rates. Two bandwidth scheduling algorithms are proposed. One is a greedy algorithm that works for sensors with limited computational capability. The other is the UMinMax scheduling algorithm that has better scalability and power-saving performance but is more computationally intensive. The third issue addressed in this proposal is how to achieve location determination and tracking by a mix of high-precision but expensive and lower-precision but cost-effective sensors. This is achieved by a novel collaborative location determination scheme ColLoc that can integrate different types of distance measurements into a location estimation algorithm by weighing them according to their precision levels. Through this, a localization service can be both cost-effective and sufficiently accurate. Fourth, in order to minimize the effects of long network latency when the body network scales up, we propose ControlInGateway, an architectural feature that allows a control application to be executed inside the network gateway without the host's involvement. With ControlInGateway, a wireless system could achieve the same control quality as a wired system.

This work has two objectives: a) to predict people's future locations, and b) to predict when they will be at given locations. Current location-based applications react to the user's current location. The progression from location-awareness to location-prediction can enable the next generation of proactive, context-predicting applications. Existing location-prediction algorithms predict someone's next location. In contrast, this dissertation predicts someone's future locations. Existing algorithms use a sequence of locations and predict the next location in the sequence. This dissertation incorporates temporal information as timestamps in order to predict someone's location at any time in the future. Sequence predictors based on Markov models have been shown to be effective predictors of someone's next location. This dissertation applies a Markov model to two-dimensional, timestamped location information to predict future locations. This dissertation also predicts when someone will be at a given location. These predictions can support presence or understanding co-workers’ routines. Predicting the times that someone is going to be at a given location is a very different and more difficult problem than predicting where someone will be at a given time. A location-prediction application may predict one or two key locations for a given time, while there could be hundreds of correct predictions for times of the day that someone will be in a given location. The approach used in this dissertation, a heuristic model loosely based on Market Basket Analysis, is the first to predict when someone will arrive at any given location. The models are applied to sparse, WiFi mobility data collected on PDAs given to 275 college freshmen. The location-prediction model predicts future locations with 78-91% accuracy. The temporal-prediction model achieves 33-39% accuracy. If a tolerance of plus/minus twenty minutes is allowed, the prediction rates rise to 77%-91%. This dissertation shows the characteristics of the timestamped, location data which lead to the highest number of correct predictions. The best data cover large portions of the day, with less than three locations for any given timestamp.

Traffic congestion, high gas price and inadequate public transportation are major challenges for any country, business or individual. The traditional approach to solving these problems has been to improve public transportation and use greener energy. These approaches require huge investment, research and time, and can only be carried out by governments or businesses. An alternative solution seeks to reduce the number of vehicles on the road based on ridesharing. Nevertheless, ridesharing is not a popular form of public transportation. A ridesharing application involves planning and collaboration in setting up rides. Some of the concerns of users of these applications are social discomfort that arises due to lack of trust amongst co-passengers and inconvenience in scheduling rides. In this work we are trying to understand the perceptions of trust in a software application that integrates collaboration and social networks. We conducted a rideshare needs assessment survey conducted within the Virginia Tech community. The purpose behind this survey was to understand commuters travel patterns, their needs and to identify their preferences for private vehicles and public transit for a variety of travel needs. The survey results indicate that users are willing to increase participation in ridesharing programs if three core issues are addressed trust, convenience and incentives. Based on the results and analysis of our survey results we present an iPhone based ridesharing application that would leverage social networks to embed trust. To overcome the complexity in scheduling rides, we have made our application available on mobile phones (iPhone) so that users are connected on the go and make use of GPS for location awareness to plan their rides instantaneously. Our social software application is easy and intuitive to use, helps users find trusted rideshares and reduces the carbon footprint of the individual.

The appropriate framework to capture and share location information with mobile applications enable the development of interfaces and interface techniques that empower users to obtain and share information on the go. As such, the work in this thesis makes two major contributions. First is the SeeVT framework, a locative backbone that uses currently-available data and equipment in the Virginia Tech and Blacksburg VA environments (e.g., wireless signal triangulation, GPS signals) to make available to applications the location of the device in use. Applications built on this framework have available knowledge of the region in which the user's device is located. Second is a set of four applications built on the SeeVT framework: SeeVT – Alumni Edition (a guide for alumni returning to campus, often after lengthy absences), the Newman Project (a library information system for finding books and other library resources), VTAssist (a information sharing system for disabled users), and SeeVT-Art (a guide for users in our local inn and conference center to learn about the art on display). Key in this contribution is our identification and discussion of three interface techniques that emerged from our development efforts: an images-first presentation of information, a lightweight mobile augmented reality style of interaction, and locative content affordances that provide ways to quickly input focused types of information in mobile situations.

The mature textile industry coupled with our familiarity and comfort level with fabrics and the possibility of seamless integration of electronic components such as sensors, processors, and power sources in the fabric opens up a new dimension of computing. The electronic textile presents a suitable substrate over which numerous applications can be developed. Location awareness is one such application that can reap the benefits of e-textiles such that it can be widely deployed at a reasonable cost for assisting visually impaired people or to provide navigational help during emergency situations. This thesis describes an autonomous, wearable location awareness system that will determine a user's location within a building given a map of that building. The thesis examines the issues, constraints, and challenges concerning the design of such a system. The two-part location awareness algorithm computes the location and orientation within a room as well as determines the user's movement between rooms. The efficacy of the proposed system is demonstrated with a wearable prototype.