Data Mining for the Internet of Things: Literature Review and Challenges

By Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, Xiaohui Rong. Int. J. Distributed Sens. Networks, 2015

539 citations recorded by the source corpus.

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/0d69ad95c0f7cd9a318f9fa0de77834ecf4680d4

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Citations in the corpus

Network Intrusion Detection for IoT Security Based on Learning Techniques (2019, background): “For the first intersection between IoT and learning domain, [20]–[23] have significant contributions.”
A survey on security and privacy issues of blockchain technology (2018, background): “IoT is defined as a system of interconnected computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction [8, 9].”
Machine learning for Internet of Things data analysis: A survey (2017, methodology): “[11] introduced a systematic method for reviewing data mining knowledge and techniques in most common applications.”
A review of industrial wireless networks in the context of Industry 4.0 (2015, background): “Meanwhile, new information and communication technologies (ICTs), such as the industrial cloud [76], big data [19, 93], wireless cloud networks [75, 104], industrial internet of things [21] and high performance embedded system [74] have been introduced into the manufacturing industry to meet the dem”
State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing (2016, methodology): “In [80] the data mining techniques are divided in five categories: classification, clustering, association analysis, time series analysis and outlier 895”
Big data privacy: a technological perspective and review (2016, background): “Also with the rapid development of IoT, there are lots of challenges when IoT and big data come; the quantity of data is big but the quality is low and the data are various from different data sources inherently possessing a great many different types and representation forms, and the data is hetero”
Implementing Smart Factory of Industrie 4.0: An Outlook (2016, background): “Recently, the emerging technologies (e.g., Internet of Things (IoT) [1–3], wireless sensor networks [4, 5], big data [6], cloud computing [7–9], embedded system [10], and mobile Internet [11]) are being introduced into the manufacturing environment, which ushers in a fourth industrial revolution.”
Text Mining in Big Data Analytics (2020, background): “There is heterogeneity, noise, and variety, and mining techniques cannot be easily modified to adjust to big data environments [110].”
Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges (2017, methodology): “Naïve, selective naïve, semi-naïve Bayes, and Bayes multi-nets are the proposed categories for classiﬁcation [48].”