Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT

被引:19
|
作者
Singh, Sushil Kumar [1 ]
Cha, Jeonghun [1 ]
Kim, Tae Woo [1 ]
Park, Jong Hyuk [1 ]
机构
[1] Seoul Natl Univ Sci & Technol, Dept Comp Sci & Engn, SeoulTech, Seoul 01811, South Korea
关键词
machine learning; big data analysis; extreme learning machine; IoT; security; and privacy; INTRUSION DETECTION SYSTEM; DATA ANALYTICS; NETWORKS; PERFORMANCE; PREDICTION;
D O I
10.2298/CSIS200330012S
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
For the advancement of the Internet of Things (IoT) and Next Generation Web, various applications have emerged to process structured or unstructured data. Latency, accuracy, load balancing, centralization, and others are issues on the cloud layer of transferring the IoT data. Machine learning is an emerging technology for big data analytics in IoT applications. Traditional data analyzing and processing techniques have several limitations, such as centralization and load managing in a massive amount of data. This paper introduces a Machine Learning Based Distributed Big Data Analysis Framework for Next Generation Web in IoT. We are utilizing feature extraction and data scaling at the edge layer paradigm for processing the data. Extreme Learning Machine (ELM) is adopting in the cloud layer for classification and big data analysis in IoT. The experimental evaluation demonstrates that the proposed distributed framework has a more reliable performance than the traditional framework.
引用
收藏
页码:597 / 618
页数:22
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