Name
Abstract | ||
|---|---|---|
In Ocean of Things, information prediction is an important part of marine data processing. Due to a variety of marine acquisition devices, the distribution of marine information is discrete. The marine area is wide, and the resolution of the collected data is small, which causes the IoT device to accurately calculate the information of a small sea area. Processing discrete and sparse data is the main job of marine information processing equipment. The main purpose of this paper is to predict the information of the blind area based on the collected data. In order to increase the resolution of sea surface information, the availability of marine data in the Internet of Things is improved. This paper proposes a method for predicting information on marine blind spots. This method is based on the improved machine learning algorithm FCM. This method is used to predict sea surface temperature and improve the resolution of sea surface data. Finally, this paper has done a number of simulation experiments, based on different methods to predict the sea surface temperature. The experimental results are discussed and the performance of the proposed method is analyzed. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/ICNC47757.2020.9049785 | 2020 International Conference on Computing, Networking and Communications (ICNC) |
Keywords | DocType | ISSN |
SST,Improved FCM,Data resolution,Ocean of Things,Machine learning | Conference | 2325-2626 |
ISBN | Citations | PageRank |
978-1-7281-4906-6 | 0 | 0.34 |
References | Authors | |
10 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiachen Yang | 1 | 362 | 29.01 |
| Jiabao Wen | 2 | 16 | 4.61 |
| Bin Jiang | 3 | 85 | 13.70 |
| Houbing Song | 4 | 1771 | 172.26 |
| Fanhui Kong | 5 | 0 | 0.68 |
| Zhizhuo Zhen | 6 | 0 | 0.68 |