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Data Mining for Internet of Things: A Survey
Abstract
It sounds like mission impossible to connect everything on the Earth together via Internet, but Internet of Things (IoT) will dramatically change our life in the foreseeable future, by making many "impossibles" possible. To many, the massive data generated or captured by IoT are considered having highly useful and valuable information. Data mining will no doubt play a critical role in making this kind of system smart enough to provide more convenient services and environments. This paper begins with a discussion of the IoT. Then, a brief review of the features of "data from IoT" and "data mining for IoT' is given. Finally, changes, potentials, open issues, and future trends of this field are addressed.
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“Data mining and machine learning approaches have been used for IoT data processing and analysis [52], [53].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [52]
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BackgroundAn Effective IoT Service-to-Interface Assignment Algorithm via Search Economics
Tsai, C., & Liu, S.-J. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2796310 · Cited by 13
“Early studies on IoT are focused on protocols, standards, overall reviews [1]–[4], system architectures [5], and even how to use data mining technologies to find out useful information from IoT systems or environments [6].”
BackgroundSelf-citationA Realization of Cyber-Physical Manufacturing Control System Through Industrial Internet of Things
Lin, Y., Lan, C.-B., & Huang, C. (2019) · Procedia Manufacturing · doi:10.1016/j.promfg.2020.01.449 · Cited by 13
Saguil, D., & Azim, A. (2020) · IEEE Access · doi:10.1109/access.2020.2981411 · Cited by 13
“Fog nodes are a type of edge device that can provide the services of cloud servers, but at a ‘‘grounded’’ level [37].”
BackgroundDistributed Differentially Private Stochastic Gradient Descent: An Empirical Study
Hegedűs, I., & Jelasity, M. (2016) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp.2016.19 · Cited by 13
“This means that personal information can leak only if the corresponding node is directly compromised.”
BackgroundData mining techniques for IoT and big data — A survey
Shobanadevi, A., & Maragatham, G. (2017) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2017.8389260 · Cited by 13
“There are not enough efficient analysis tools, and so all the systems, will definitely be merged by this large amount of data [1].”
BackgroundMethodInfluentialUllah, A., Aznaoui, H., Sebai, D., Abualigah, L., Alam, T., & Chakir, A. (2023) · Wireless Personal Communications · doi:10.1007/s11277-023-10817-2 · Cited by 12
RETRACTED: An intrusion detection system for health-care system using machine and deep learning
Pande, S. D., Khamparia, A., & Gupta, D. K. (2021) · World Journal of Engineering · doi:10.1108/wje-04-2021-0204 · Cited by 12
RetractedIntelligent data-intensive IoT: A survey
Xiao, B., Rahmani, R., Li, Y., Gillblad, D., & Kanter, T. G. (2016) · IEEE International Conference on Computer and Communications (ICCC) · doi:10.1109/compcomm.2016.7925122 · Cited by 12
Kumar, S., & Raza, Z. (2017) · International Journal of Systems and Service-Oriented Engineering · doi:10.4018/ijssoe.2017070103 · Cited by 12
Smart Cities – Enabling Technologies for Future Living
Wlodarczak, P. (2017) · Springer optimization and its applications · doi:10.1007/978-3-319-65338-9_1 · Cited by 12
Development of an oneM2M-compliant IoT Platform for Wearable Data Collection
Ahn, I.-Y., Sung, N.-M., Lim, J.-H., Seo, J., & Yun, I. D. (2019) · KSII Transactions on Internet and Information Systems · doi:10.3837/tiis.2019.01.001 · Cited by 12
“I nternet of Things (IoT) often refers to a convergence technology including devices, networks, platforms and services and also a future internet technology enabling advanced services by interconnecting physical and virtual things, collecting and using a lot of data from them [1]-[4].”
BackgroundThe Internet of things: a survey and outlook
Perrone, G., Vecchio, M., Ser, J. D., Antonelli, F., & Kapoor, V. (2019) · Institution of Engineering and Technology eBooks · doi:10.1049/pbce122e_ch1 · Cited by 12
“A yet one more degree of complexity is posed by the eventual heterogeneity of such information flows, which has encouraged the adoption of information fusion schemes at different levels of IoT systems [42].”
BackgroundInternet of Things: Overview, Sources, Applications and Challenges
Oweis, N. E., Aracena, C., George, W., Oweis, M., Soori, H., & Snåšel, V. (2016) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-29504-6_7 · Cited by 12
SoK: Applying Blockchain Technology in Industrial Internet of Things.Preprint
Wang, G. (2021) · IACR Cryptology ePrint Archive · Cited by 12
An Efficient Memristor-based Distance Accelerator for Time Series Data Mining on Data Centers
Xu, X., Zeng, D., Xu, W., Shi, Y., & Hu, Y. H. (2017) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1145/3061639.3062200 · Cited by 11
“…in real-time with high energy efficiency Classification, clustering and frequency pattern mining are three main data mining tasks for time series [27 The computational bottleneck of these methods is the calculation of distance function, which is used to evaluate the similarity of two time series…”
MethodA Clustering Analysis Method With High Reliability Based on Wilcoxon-Mann-Whitney Testing
Cheng, Y., Jia, W., Chi, R., & Li, A. (2021) · IEEE Access · doi:10.1109/access.2021.3053244 · Cited by 11
“K-means [8], [9] adjusts the clusters and their mean values based on the distances between objects and clusters mean values in each iteration.”
MethodWang, Y., Li, S., Meng, K., Zhang, Y., Fang, Z., & Sun, S. (2024) · ACS Sustainable Chemistry & Engineering · doi:10.1021/acssuschemeng.3c06977 · Cited by 12
Hossain, M. A., Islam, A., Le, N. T., Lee, Y. T., Lee, H. W., & Jang, Y. M. (2016) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147716657926 · Cited by 11
“eue belonging to signage display, d is the summation of the queuing probability of the request when there are no available channels for the user, and it can be termed as R c (d), as shown in equation (4). Besides, the overall probability of rejecting a request of the entire system, R C, can be expressed by equation (5…”
BackgroundGreen Internet of Things: A Survey of Enabling Techniques
Nazir, T., & Banday, M. T. (2018) · International Conference Automation and Computational Engineering · doi:10.1109/icace.2018.8686974 · Cited by 11
“Thus, huge processing would be involved at the Data Centers that would consume enormous amount of energy [1] and emit enormous volume of carbon dioxide [2].”
Background
Metadata
- Authors
- Tsai, C.-W., Lai, C.-F., Chiang, M.-C., & Yang, L. T.
- Affiliations
- Chia Nan University of Pharmacy and Science (TW)
- National Chung Cheng University (TW)
- National Sun Yat-sen University (TW)
- Huazhong University of Science and Technology (CN)
- Venue
- IEEE Communications Surveys & Tutorials
- Published
- 2014
- Volume
- 16
- Issue
- 1
- Pages
- 77-97
- ISSN
- 1553-877X (ISSN-L)
- 2373-745X
- OpenAlex
- W2041785419
- Cited by
- 620
- References
- 156
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 29 · 2023: 35 · 2022: 44 · 2021: 58 · 2020: 73 · 2019: 87 · 2018: 86 · 2017: 92 · 2016: 55 · 2015: 25 · 2014: 11
- FWCI
- 168.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Communications Surveys & Tutorials (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
- Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- Internet of Things, data mining, IoT data, big data, smart environments, open problems, future trends
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