A peer-to-peer collaboration framework for multisensor 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. © Springer Science+Business Media B.V. 2008.
CITATION STYLE
Lee, P., Jayasumana, A. P., Lim, S., & Chandrasekar, V. (2008). A peer-to-peer collaboration framework for multi-sensor data fusion. In Novel Algorithms and Techniques in Telecommunications, Automation and Industrial Electronics (pp. 207–213). https://doi.org/10.1007/978-1-4020-8737-0_37
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