Abstract
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 in the result of 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 considering application- specific needs. We also present a dynamic peer-selection algorithm, called Best Peer Selection (BPS) that chooses a set of peers based on their computation and communication capabilities to minimize the execution time required for processing data per integration algorithm. Simulation-based results show that BPS can deliver a significant improvement in execution time for multi-radar data fusion.
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Lee, P., Jayasumana, A.P., Lim, S., Chandrasekar, V. (2008). A Peer-to-Peer Collaboration Framework for Multi-sensor Data Fusion. In: Sobh, T., Elleithy, K., Mahmood, A., Karim, M.A. (eds) Novel Algorithms and Techniques In Telecommunications, Automation and Industrial Electronics. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8737-0_37
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DOI: https://doi.org/10.1007/978-1-4020-8737-0_37
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