文献类型： 外文期刊

作者： Kuang, Liang ¹ ; Shi, Pei ³ ; Hua, Chi ¹ ; Chen, Beijing ² ; Zhu, Hui ⁵ ;

作者机构： 1.Jiangsu Vocat Coll Informat Technol, Sch IoT Engn, Wuxi 214153, Jiangsu, Peoples R China

2.Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Peoples R China

3.Nanjing Univ Informat Sci & Technol, Binjiang Coll, Wuxi 214105, Jiangsu, Peoples R China

4.Chinese Acad Fishery Sci, Freshwater Fisheries Res Ctr, Wuxi 214081, Jiangsu, Peoples R China

5.South China Univ Technol, Sch Elect & Informat Engn, Guangzhou 510641, Peoples R China

关键词： Aquaculture; Predictive models; Water quality; Meteorology; Monitoring; Indexes; Wireless sensor networks; Sensor networks; dissolved oxygen prediction; edge computing; water quality monitoring; extreme learning machine

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2020 年 8 卷

页码：

收录情况： SCI

摘要： Water quality monitoring using Wireless Sensor Networks (WSNs) is essential in aquaculture water quality management. In the field of water quality monitoring, dissolved oxygen (DO) is a key parameter, and its prediction can provide decision support for aquaculture production, thereby reducing farming risk. However, it is difficult to build a precise prediction model, and existing methods of DO prediction neglect the importance of analyzing DO content. To address this problem, this study proposes a hybrid DO prediction model, named KIG-ELM, which is composed of K-means, improved genetic algorithm (IGA), and extreme learning machine (ELM). This model is based on edge computing architecture, in which data acquisition, processing and dissolved oxygen prediction are distributed in sensing nodes, routing nodes and server respectively. Sensing technique and clustering operation are applied in the process of data acquisition and processing. Meanwhile, an optimized extreme learning machine is implemented for DO prediction. We evaluate the efficiency and accuracy of proposed prediction approach in a practical aquaculture on massive water quality data. Experimental results show that the hybrid model achieves significant prediction results and can meet the needs of practical production and management.