Abstract
Networks of thousands of sensors present a feasible and economic solution to some of our most challenging problems, such as real-time traffic modeling, military sensing and tracking. Many research projects have been conducted by different organizations regarding wireless sensor networks; however, few of them discuss how to estimate missing sensor data. In this research we present a novel data estimation technique based on association rules derived from closed frequent itemsets generated by sensors. Experimental results compared with the existing techniques using real-life sensor data show that closed itemset mining effectively imputes missing values as well as achieves time and space efficiency.
This research is partially supported by the NASA grant No. NNG05GA30G and a research grant from the United States Department of Defense.
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Jiang, N., Gruenwald, L. (2007). Estimating Missing Data in Data Streams. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_89
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DOI: https://doi.org/10.1007/978-3-540-71703-4_89
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