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Information Science and Technology Center (ISTeC)

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These digital collections include materials from ISTeC Distinguished Lectures and Industrial Advisory Council presentations, and publications, grants, and student research posters from ISTeC.

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Browsing Information Science and Technology Center (ISTeC) by Subject "application-aware overlay network"

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    Application-aware in-network service deployment for collaborative adaptive sensing of the atmosphere (CASA)
    (Colorado State University. Libraries, 2008) Lee, Panho, author; Banka, Tarun, author; Lim, Sang-hun, author; Jayasumana, Anura P., author; Chandrasekar, V., author
    An Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) funded by the National Science Foundation, seeks to revolutionize the way we detect, monitor and predict atmospheric phenomena by creating a dense network of small, low-cost, low-power radars that could collaboratively and adaptively sense the lower atmosphere. Such a network is expected to provide more timely and accurate forecasts for tornadoes, flash floods, and other hazardous weathers. In addition, the networked radars can offer improved accuracies and more specific inferences that could not be achieved by the use of a single long-range radar. In CASA, multiple end users may be present that have distinct sensing, communication and computation requirements for their operations. In addition, the underlying network infrastructure may itself be subjected to adverse conditions due to severe weather and link degradation/outage along wired and wireless links. We use overlay networking to provide acceptable quality of service (QoS) and robust data transport service for the CASA end-users. At CSU, we have developed an AWON (Application-aWare Overlay Networks) architecture for deploying application-aware services in an overlay network to best meet the end-users' QoS requirements over the available networking infrastructure; based on this, we have implemented an application-aware multicast service for CASA. We also present a multi-sensor fusion framework which can provide a mechanism for selecting a set of data for data fusion considering application-specific needs, and a distributed processing scheme to minimize the execution time required for processing data per integration algorithm.