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Graduate Exam Abstract


Pan ho Lee

Ph.D. Final

January 26, 2009, 2:00-4:00 pm

Engr. Building B103

Application-aware in-network Service and Data Fusion Frameworks for Distributed Adaptive Sensing Systems


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 and a multi-sensor data fusion 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 a general-purpose 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 application programming interface (API) facilitates development of applications within the architectural framework, and supports the configuration of overlay nodes for the in-network application-aware processing. The API also enables communication between the applications and the overlay routing protocol for the desired QoS support. We demonstrate the efficacy of the proposed framework by considering a DCAS application. In the DCAS application, high-bandwidth radar data is distributed to multiple end users with heterogeneous QoS requirements and different network conditions. 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. Data from multiple sensors enlarges the field of view and increases the certainty and precision of estimates in the CASA system. In this context, multi-sensor data fusion is increasingly common and is an essential constituent of the sensor applications. This dissertation also presents the design and implementation of an architectural framework for timely and accurate processing of radar data fusion algorithms in a networked-radar environment. 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 in the CASA IP-1 testbed. As a part of this research, a peer-to-peer collaboration framework for multi-sensor data fusion in resource-rich radar networks is presented. In the multi-sensor data fusion, data needs to be combined in such a manner that the real-time requirement of the sensor application is met. In addition, the desired accuracy of the result from the multi-sensor fusion has to be obtained by selecting a proper set of data from multiple radar sensors. A mechanism for selecting a set of data for data fusion is provided with the consideration application-specific needs. We have also proposed a dynamic peer-selection algorithm, Best Peer Selection (BPS) that selects a set of peers based on their computation and communication capabilities to minimize the execution time required to process data per integration algorithm. Simulation-based results show that BPS can deliver a significant improvement in the execution time for multi-radar data fusion. 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. The ubiquity of time scale-invariant burstiness observed across the network produces excessive network latencies. The multi-sensor data fusion applications require synchronizing a set of correlated data before fusion processing can begin. The analytical model considers the network delay due to the self-similar cross-traffic and latency for data synchronization. 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. The model can be used to provision of network bandwidth and for developing data synchronization strategy for the multi-sensor fusion applications.

Adviser: Anura P. Jayasumana
Co-Adviser: N/A
Non-ECE Member: Yashwant K. Malaiya, CS
Member 3: V Chandrasekar, ECE
Addional Members: N/A

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