Name
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1007/978-3-319-94268-1_59 | WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS (WASA 2018) |
Keywords | Field | DocType |
Wireless Sensor Networks, Big sensory data, Relative kernel dataset | Resource consumption,Kernel (linear algebra),Computer science,Terabyte,Petabyte,Explosive material,Real-time computing,Sensory system,Wireless sensor network,Computation,Distributed computing | Conference |
Volume | ISSN | Citations |
10874 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 13 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tongxin Zhu | 1 | 21 | 3.81 |
| Jinbao Wang | 2 | 142 | 11.58 |
| Siyao Cheng | 3 | 3 | 2.90 |
| Yingshu Li | 4 | 671 | 53.71 |
| Jianzhong Li | 5 | 3196 | 304.46 |