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ABSTRACT: The Cooperative Communities (CoCo) learning framework leverages everyday social connections between people to personalize classification models. By exploiting social networks, CoCo spreads the burden of providing training data over an entire community.
IEEE Pervasive Computing 05/2011; · 1.55 Impact Factor
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ABSTRACT: Technological advances in sensing, computation, storage, and communications will turn the near-ubiquitous mobile phone into a global mobile sensing device. People-centric sensing will help drive this trend by enabling a different way to sense, learn, visualize, and share information about ourselves, friends, communities, the way we live, and the world we live in. It juxtaposes the traditional view of mesh sensor networks with one in which people, carrying mobile devices, enable opportunistic sensing coverage. In the MetroSense Project's vision of people-centric sensing, users are the key architectural system component, enabling a host of new application areas such as personal, public, and social sensing.
IEEE Internet Computing 08/2008; 12(4):12-21. · 2.00 Impact Factor
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ABSTRACT: A transmitter in a wireless network that uses CSMA, a simple carrier sensing-based MAC protocol, to determine the likelihood of successful packet reception at the intended receiver can easily be misled. At the same time, CSMA variants and hybrid MAC protocols based at least in part on carrier sensing have become the de facto standard in wireless sensor networks, underscoring a need to improve its performance. We propose to enhance the de facto state of carrier sensing-based MACs in wireless sensor networks by using low cost channel feedback combined with a learning approach to try to better predict the probability of a successful reception, on a per-receiver basis. We show results from an experimental wireless sensor network testbed, where our proposal E-CSMA (Enhanced CSMA) provides up to a 55% improvement in network performance.
INFOCOM 2007. 26th IEEE International Conference on Computer Communications. IEEE; 06/2007
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ABSTRACT: We describe our experiences deploying BikeNet, an ex-tensible mobile sensing system for cyclist experience map-ping leveraging opportunistic sensor networking principles and techniques. BikeNet represents a multifaceted sensing system and explores personal, bicycle, and environmental sensing using dynamically role-assigned bike area network-ing based on customized Moteiv Tmote Invent motes and sensor-enabled Nokia N80 mobile phones. We investigate real-time and delay-tolerant uploading of data via a num-ber of sensor access points (SAPs) to a networked reposi-tory. Among bicycles that rendezvous en route we explore inter-bicycle networking via data muling. The repository provides a cyclist with data archival, retrieval, and visual-ization services. BikeNet promotes the social networking of the cycling community through the provision of a web por-tal that facilitates back end sharing of real-time and archived cycling-related data from the repository. We present: a de-scription and prototype implementation of the system archi-tecture, an evaluation of sensing and inference that quantifies cyclist performance and the cyclist environment; a report on networking performance in an environment characterized by bicycle mobility and human unpredictability; and a descrip-tion of BikeNet system user interfaces. Visit [4] to see how the BikeNet system visualizes a user's rides.
