Application-aware in-network service and data fusion frameworks for distributed adaptive sensing systems
Date
2009
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Abstract
Distributed Collaborative Adaptive Sensing (DCAS) systems are emerging for applications, such as detection and prediction of hazardous weather using a network of radars. Collaborative Adaptive Sensing of the Atmosphere (CASA) is an example of these emerging DCAS systems. CASA is based on a dense network of weather radars that operate collaboratively to detect tornadoes and other hazardous atmospheric conditions. This dissertation presents an application-aware data transport framework to meet the data distribution/processing requirements of such mission-critical sensor applications over best-effort networks. Our application-aware data transport framework consists of overlay architecture and a programming interface. The architecture enables deploying application-aware in-network services in an overlay network to allow applications to best adapt to the network conditions. The programming interface facilitates development of applications within the architectural framework. We demonstrate the efficacy of the proposed framework by considering a DCAS application. We evaluate the proposed schemes in a network emulation environment and on Planetlab, a world-wide Internet test-bed. The proposed schemes are very effective in delivering high quality data to the multiple end users under various network conditions. This dissertation also presents the design and implementation of an architectural framework for timely and accurate processing of radar data fusion algorithms. The preliminary version of the framework is used for real-time implementation of a multi-radar data fusion algorithm, the CASA network-based reflectivity retrieval algorithm. As a part of this research, a peer-to-peer (P2P) collaboration framework for multi-sensor data fusion is presented. Simulation-based results illustrate the effectiveness of the proposed P2P framework. As multi-sensor fusion applications have a stringent real-time constraint, estimation of network delay across the sensor networks is important, particularly as they affect the quality of sensor fusion applications. We develop an analytical model for multi-sensor data fusion latency for the Internet-based sensor applications. Time scale-invariant burstiness observed across the network produces excessive network latencies. The analytical model considers the network delay due to the self-similar cross-traffic and latency for data synchronization for data fusion. A comparison of the analytical model and simulation-based results show that our model provides a good estimation for the multi-sensor data fusion latency.
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Subject
data fusion
distributed adaptive sensing
overlay networks
peer-to-peer
electrical engineering