@inproceedings{3ed91e5355624277a3628dab96e0a21c,
title = "Retrieving the relative kernel dataset from big sensory data for continuous query",
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 KQ 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.",
keywords = "Big sensory data, Relative kernel dataset, Wireless Sensor Networks",
author = "Tongxin Zhu and Jinbao Wang and Siyao Cheng and Yingshu Li and Jianzhong Li",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 13th International Conference on Wireless Algorithms, Systems, and Applications, WASA 2018 ; Conference date: 20-06-2018 Through 22-06-2018",
year = "2018",
doi = "10.1007/978-3-319-94268-1\_59",
language = "英语",
isbn = "9783319942674",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "720--732",
editor = "Wei Cheng and Wei Li and Sriram Chellappan",
booktitle = "Wireless Algorithms, Systems, and Applications - 13th International Conference, WASA 2018, Proceedings",
address = "德国",
}