The DOI matches its Crossref record; 152 of the 156 references it cites were found in a registry or an index.
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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Data mining: concepts and techniques
Sharma, R., Shah, K., Shirahatti, Y., Patel, S., Sharma, R., Shah, K., Patel, S., Sharma, R., & Han, J. (2012) · Choice Reviews Online · doi:10.5860/choice.49-3305 · Cited by 28,665
(1967) · Cited by 23,719
Assessment of Older People: Self-Maintaining and Instrumental Activities of Daily Living
Lawton, M. P., & Brody, E. M. (1969) · The Gerontologist · doi:10.1093/geront/9.3_part_1.179 · Cited by 18,540
Wireless sensor networks: a survey
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002) · Computer Networks · doi:10.1016/s1389-1286(01)00302-4 · Cited by 17,523
The Internet of Things: A survey
Atzori, L., Iera, A., & Morabito, G. (2010) · Computer Networks · doi:10.1016/j.comnet.2010.05.010 · Cited by 15,613
Energy-efficient communication protocol for wireless microsensor networks
Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2000) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.1109/hicss.2000.926982 · Cited by 14,946
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Jain, A. K., Murty, M. N., & Flynn, P. J. (1999) · ACM Computing Surveys · doi:10.1145/331499.331504 · Cited by 13,925
Internet of Things (IoT): A vision, architectural elements, and future directions
Gubbi, J., Buyya, R., Marusic, S., & Palaniswami, M. (2013) · Future Generation Computer Systems · doi:10.1016/j.future.2013.01.010 · Cited by 12,293
A training algorithm for optimal margin classifiers
Boser, B. E., Guyon, I. M., & Vapnik, V. N. (1992) · Proceedings of the fifth annual workshop on Computational learning theory · doi:10.1145/130385.130401 · Cited by 11,992
Research works printed but not linked: 12
- 2012, Internet of Things (IoT) & M2M communication market - advanced technologies, future cities & adoption trends, roadmaps & worldwide forecasts 2012-2017, Tech Rep
- macmanus, 2009, Top 10 internet of things products of 2009
- 2012, Instrumental activities of dailyliving (IADL)
- 2012, 50 Sensor Applications for A Smarter World
- nolan, 1979, Managing the crises in data processing, Harvard Business Review, 57, 115
- gantz, 2011, Extracting Value from Chaos
- 2012, Activities of daily living (ADL)
- 2008, INFSO D.4 Networked Enterprise & RFID INFSO G.2 Micro & Nanosystems, Co-operation with the Working Group RFID of the ETP EPOSS Internet of Things in 2020 Roadmap for the Future
- sundmaeker, 2010, Vision and Challenges for Realising the Internet of Things
- 2011, Special Online Collection Dealing with Data
- 2012, Shanghai Institute of Microsystem and Information Technology
- 2012, Massachusetts Institute of Technology
Cited by
Sorted by citations
Items 601–618 of 618
Chauhan, G. S., Nahta, R., Upadhyay, A., & Meena, Y. K. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-7814-4_2
Alateyah, S. A. (2024) · Journal of Computer Science · doi:10.3844/jcssp.2024.602.609
“Every object on Earth should be connected to the internet, according to IOT (Tsai et al ., 2013; Bhatia and Patel, 2015; Chen et al ., 2015b).”
BackgroundIOT - Enabled Digital Vehicle Parking Systems using Machine Learning
Sanghvi, A. (2024) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2024.61990
Ni’am, F., Arengga, D., & Sendari, S. (2024) · ALINIER Journal of Artificial Intelligence & Applications · doi:10.36040/alinier.v5i1.5105
Applications of IoT and data mining techniques for diabetes monitoring
Yadav, K., & Hasija, Y. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-95686-4.00014-9
Monteleone, M., & Postiglione, A. (2024) · Frontiers in artificial intelligence and applications · doi:10.3233/faia241455
Sadikan, S. F. N., Aziz, M. L. H. A., Umor, M. A. S., Mahzan, S., Marjudi, S. B., Salamat, M. A., & Dunne, R. S. (2025) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2942/1/012041
“These data are mined and modelled to elucidate the relationships between inputs and outputs (Tsai et. al., 2014).”
A high-performance data analytics method for significant pattern discovery in cognitive IoT
Jha, V., & Tripathi, P. (2025) · Quality & Quantity · doi:10.1007/s11135-025-02180-0
“From the golden value pair extraction in the CIoT scenario, the suggested method extracts the significant pattern.”
InfluentialJha, V., & Tripathi, P. (2025) · Progress in Artificial Intelligence · doi:10.1007/s13748-025-00380-1
“The information gleaned from these raw data sets is called CIoT infrastructure data, as described in [ 25], while the data collected by the CIoT environment is called CIoT environment data.”
Fluid intelligence: a novel strategy for adaptive learning in cognitive IoT
Jha, V., & Tripathi, P. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-08059-z
“Existing methods and software are inadequate to deal with the new paradigm of huge data, whose complexity exceeds the capabilities of existing computational approaches [12].”
Retroductive reasoning: a data-driven intelligent method for knowledge discovery using cognitive IoT
Jha, V., & Tripathi, P. (2025) · Knowledge and Information Systems · doi:10.1007/s10115-025-02649-2
“Both the infrastructure, which includes information about devices and networks, and the features of the artificial environment that underpins the IoT are the origins of the raw data discussed earlier [24].”
Real-Time Cloud Database Communication With Xamarin Cross-Platform: Integrated Smart Home System
TOSUNOĞLU, E., & Üstün, A. B. (2021) · DergiPark (Istanbul University)
Building Trust in the Web of Things: Decentralized Identity Management with Blockchain
Mssassi, S., & Kalam, A. A. E. (2026) · Signals and communication technology · doi:10.1007/978-3-032-07671-7_7
Mehr, S. Y., & Phadke, A. (2026) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc67433.2026.11393743
“By integrating GenAI, our framework ensures that network operators can audit, understand, and trust allocation decisions, effectively bridging the gap between predictive performance and operational transparency [5]–[7].”
Reliable Platform Using Distributed Ledger Technology For IoT-based Industrial Environment
Hellani, H. (2022) · theses.fr (ABES) · doi:10.70675/e8526cb7zb9acz498cza7c0zcb8c26056534 · Cited by 1
Machine Learning-Based Sentiment Analysis for Foreign Visitors of Anıtkabir
YİĞİT, Ş., & Uzunali, A. (2026) · Artvin Çoruh Üniversitesi Orman Fakültesi Dergisi · doi:10.17474/artvinofd.1918011
Sharma, D., Kaur, I., Gupta, K., & Kaur, M. (2026) · International Conference on Circuit, Power and Computing Technologies · doi:10.1109/iccpct70290.2026.11654831
“Even though classification, clustering, and association techniques for data mining and machine learning could be effectively used for the identification of hidden but dangerous patterns, system administrators sadly lack the authority to maximize the data that is available [7].”
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
