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As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus(More)
PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the(More)
The increasing ubiquity of the mobile phone is creating many opportunities for personal context sensing, and will result in massive databases of individuals' sensitive information incorporating locations, movements, images, text annotations, and even health data. In existing system architectures, users upload their raw (unprocessed or filtered) data streams(More)
AutoGait is a mobile platform that autonomously discovers a user's walking profile and accurately estimates the distance walked. The discovery is made by utilizing the GPS in the user's mobile device when the user is walking outdoors. This profile can then be used both indoors and outdoors to estimate the distance walked. To model the person's walking(More)
Each of us has a complex and reciprocal relationship with our environment. Based on limited knowledge of this interwoven set of influences and consequences, we constantly make choices: where to live, how to go to work, what brands to buy, what to do with our leisure time. These choices evolve into patterns, and these patterns become driving functions of our(More)
Human mobility states, such as dwelling, walking or driving, are a valuable primary and meta data type for transportation studies, urban planning, health monitoring and epidemiology. Previous work focuses on fine-grained location-based mobility inference using global positioning system (GPS) data and external geo-indexes such as map information. GPS-based(More)
Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal(More)
Inferring mobility states such as being stationary, walking, or driving is critical for transportation studies, urban planning, health monitoring and epidemiology. Our goal is to build a pervasive mobility classification system using smartphones while focusing on large deployment, which poses new design requirements: low processing complexity, high energy(More)
We propose a pervasive mobility classification method using radio beacons such as Global System for Mobile communications (GSM) and Wi-Fi traces. The model adopts different classifiers depending on the densities of radio beacons in different environments. We demonstrate how coarser-grained mobility states such as being stationary, walking, or driving can be(More)
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