Abstract
With the rapid development of Wireless Sensor Networks (WSNs), the amount of sensory data manifests an explosive growth. Currently, the sensory data generated by some WSNs is more than terabytes or petabytes, which has already exceeded the computation and transmission abilities of a WSN. Fortunately, the volume of valuable data for a given query is usually small. For a given query Q, the dataset which is highly related to it is called the relative kernel dataset \(\mathcal {K}^Q\) of Q. In this paper, we study the problem of retrieving relative kernel dataset from big sensory data for continuous queries. The theoretical analysis and simulation results show that our proposed algorithms have high performance in term of accuracy and resource consumption.
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Acknowledgment
This work is partly supported by the National Natural Science Foundation of China under Grant NO. 61632010, 61502116, U1509216, 61370217, the National Science Foundation (NSF) under grant NO.1741277.
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Zhu, T., Wang, J., Cheng, S., Li, Y., Li, J. (2018). Retrieving the Relative Kernel Dataset from Big Sensory Data for Continuous Query. In: Chellappan, S., Cheng, W., Li, W. (eds) Wireless Algorithms, Systems, and Applications. WASA 2018. Lecture Notes in Computer Science(), vol 10874. Springer, Cham. https://doi.org/10.1007/978-3-319-94268-1_59
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DOI: https://doi.org/10.1007/978-3-319-94268-1_59
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