Abstract
Sensor fusion is the process of combining sensor readings from disparate resources so that the resulting information is more accurate and complete. The key challenge in sensor fusion arises from the inherent imperfection of data, commonly caused by sampling error, network respond time, imprecise measurement, and unreliable resources. Therefore, data fusion methods need to be advanced to address various aspects of data imperfections. In this paper, we first propose a novel unified data fusion framework based on rough set theory to systematically represent data granularity and imprecision. Then, we develop a cost-driven adaptive learning algorithm that can infer the optimal threshold values from data to obtain minimum cost. Our experimental study demonstrates the frameworkâs effectiveness and validity.
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Acknowledgements
The work was supported by the Enhancement Research Grant (ERG) from Office of Research Administration (SHSU).
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Zhou, B., Cho, H., Mansfield, A. (2017). Robust Sensor Data Fusion Through Adaptive Threshold Learning. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_32
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DOI: https://doi.org/10.1007/978-3-319-60042-0_32
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