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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]
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
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Yu, J., Kim, Y., Kwon, S., Kim, K., Kim, N.-S., Kim, S., & Pyo, C. (2017) · International Conference on Platform Technology and Service · doi:10.1109/platcon.2017.7883683 · Cited by 1
“Recently, deep learning has been shown to accomplish excellent performance in the real-world domains and applications [15], [16], [17], [20], [21], [22].”
InfluentialWireless Self Control System for Domestic Appliances
Patil, A., Pallewad, T., Thakur, B., & Mane, S. N. (2017) · INTERNATIONAL JOURNAL OF ADVANCED ENGINEERING AND MANAGEMENT · doi:10.24999/ijoaem/02060032 · Cited by 1
“Smart home system is nothing but the technology used in home environment to provide comfort, security and minimum efforts by using Internet of things [3].”
BackgroundEdge Real-Time Medical Data Segmentation for IoT Devices with Computational and Memory Constrains
Bernaś, M., Płaczek, B., & Sapek, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-67077-5_12 · Cited by 1
Distributed Data Clustering in the Context of the Internet of Things
Brandão, R., & Goldschmidt, R. R. (2017) · Brazilian Symposium on Multimedia and the Web · doi:10.1145/3126858.3131579 · Cited by 1
“Porém, apesar da maioria das pesquisas em IoT apontar para a necessidade da descentralização do processamento e armazenamento de dados, diversas técnicas de mineração de dados foram desenvolvidas considerando uma visão completa dos dados [19], o que, no contexto da Internet das Coisas, implica em uma centralização do…”
BackgroundMultidimensional Wavelet Neuron for Pattern Recognition Tasks in the Internet of Things Applications
Vynokurova, O. А., Peleshko, D., Oskerko, S., Lutsan, V., & Peleshko, M. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-91008-6_7 · Cited by 1
Sparse Representation Based on the Analysis Model With Optimization on the Stiefel Manifold
Li, Y., Ding, S., Tan, B., Zhao, H., & Li, Z. (2019) · IEEE Access · doi:10.1109/access.2018.2890299 · Cited by 1
Machine Learning, Big Data, And Smart Buildings: A Comprehensive Survey.
Qolomany, B., Al‐Fuqaha, A., Gupta, A. K., Benhaddou, D., Alwajidi, S., Qadir, J., & Fong, A. C. M. (2019) · Cited by 1
Abhishek, D. P., Dinesh, N., & Prakash, S. P. S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1451-7_12 · Cited by 1
Knowledge Discovery for Sustainability Enhancement Through Design for Relevance
Benabdellah, A. C., Benghabrit, A., Bouhaddou, I., & Zekhnini, K. (2021) · Smart innovation, systems and technologies · doi:10.1007/978-981-16-3637-0_50 · Cited by 1
A Comparative Analysis of Machine Learning Algorithms For Healthcare Device Data of Social IoT
Bhavya, D., Vinod, D. S., & Prakash, S. P. S. (2019) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 1
Sridhar, M., & Srinivas, Y. (2022) · International Journal of Computer Science and Mobile Computing · doi:10.47760/ijcsmc.2022.v11i01.004 · Cited by 1
Ghayvat, H., & Mukhopadhyay, S. C. (2017) · Smart sensors, measurement and instrumentation · doi:10.1007/978-3-319-52048-3_2 · Cited by 1
(2019) · International Journal of Recent Technology and Engineering (IJRTE) · doi:10.35940/ijrte.c1129.1083s219 · Cited by 1
Industrial Guidelines for Stimulating Entrepreneurship With the Internet of Things
Zarei, M., Jamalian, A., & Ghasemi, R. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-8182-6.ch015 · Cited by 1
Machine Learning in 5G Multimedia Communications
Milovanovic, D. A., Bojkovic, Z. S., & Kukolj, D. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-7708-0.ch010 · Cited by 1
IoT rendszereket fenyegető támadások
Johanyák, Z. C., & Pásztor, A. (2023) · Gradus · doi:10.47833/2023.1.csc.001 · Cited by 3
Revolutionizing Healthcare: The Power of Machine Learning
Sharma, M., Tiwari, D., Verma, N., & Singhal, A. (2024) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9789815305128124010015 · Cited by 1
Big Coding Data: Analysis, Insights, and Applications
Rahman, M. M., Shirafuji, A., & Watanobe, Y. (2024) · IEEE Access · doi:10.1109/access.2024.3521383 · Cited by 2
“Most data mining algorithms share a common structure, comprising initialization, input and output, scanning, association rule construction, and rule update functions [52], as demonstrated in Algorithm 1.”
An energy-efficient knowledge discovery in cognitive IoT
Jha, V., & Tripathi, P. (2025) · OPSEARCH · doi:10.1007/s12597-025-00977-z · Cited by 1
“CIoT infrastructure data and CIoT environment data (such as temperature and humidity readings) are two broad categories into which the knowledge gained from this raw data can be sorted [16].”
Rule-based modeling tool for web of things applications
Park, N., Lee, H.-K., & Jang, J. (2015) · IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin) · doi:10.1109/icce-berlin.2015.7391326 · Cited by 1
Discrimination Aware Data Mining in Internet of Things (IoT)
Gorave, A., & Kulkarni, V. Y. (2017) · International Journal of Computer Applications · doi:10.5120/ijca2017912894 · Cited by 1
“Survey of researches to resolve these problems is presented in [3] [4] [5] [6] [7].”
BackgroundPattern generation from event oriented sensor data using distributed sensor transaction model
Pal, A., & Kumar, M. (2017) · Multidisciplinary International Social Networks Conference (MISNC) · doi:10.1145/3092090.3092127 · Cited by 1
“IoT data is result of interaction between the things or between things and users etc. Data generated by the IoT devices can be categorized to Big Data as this data follows the taxonomy of Big Data and also the V’s of the Big Data (Berkovich and Liao 2012).”
MethodEstrada, Y. S., León, M., Haz, L., Apolo, R. I., Moreira, J. S., & Antón-Cedeño, C. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-95162-1_51 · Cited by 1
Lücken, V., & Ascheid, G. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816637-6.00003-8 · Cited by 1
Machine Learning Techniques for Cognition of Driving Context
Hina, M. D., Soukane, A., & Ramdane-Chérif, A. (2019) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med.2019.8798589 · Cited by 1
“This project makes use of various sources, including internet of things (IoT) [10, 11], to obtain driving parameters that will make up the driving context [12].”
BackgroundGoto, M., Kobayashi, N., Ren, G., & Ogihara, M. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006208 · Cited by 1
“Most existing workflow in these engineering fields maintains a data archival tradition that requires complete data capturing, storage (both long term and short term), and comprehensive backup and recovery facilities [36, 37].”
BackgroundEsquemas de combinación de árboles de decisión como estrategia para la detección de anomalías
Quintana-Zaez, J. C., Velarde-Bedregal, H. R., Antón-Vargas, J. A., & Joaquim-Luis, G. (2020) · LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI) · doi:10.18687/laccei2020.1.1.306 · Cited by 1
Secure grid‐based density peaks clustering on hybrid cloud for industrial IoT
Sun, L., Ci, S., Liu, X., Guo, L., Zheng, X., & Luo, Y. (2020) · International Journal of Network Management · doi:10.1002/nem.2139 · Cited by 1
Narrowband IoT for Internet of Everything
Routray, S. K., & Anand, S. (2020) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-7998-4775-5.ch013 · Cited by 1
A Clustering Analysis Method Based on Wilcoxon-Mann-Whitney Testing
Cheng, Y., Jia, W., & Chi, R. (2020) · EAI International Conference on Mobile Multimedia Communications (MobiMedia) · doi:10.4108/eai.27-8-2020.2296732 · Cited by 1
Device Interaction Graph: Directed Decision Graph for Settings Auto-Completion
Kannan, R., Pathuri, S. H., & Kumar, D. (2020) · IEEE Access · doi:10.1109/access.2020.3048423 · Cited by 1
“The use of data mining for data generated by IoT enabled devices, is explained in [15].”
MethodEvaluation of Class Decomposition based on Clustering Validity and K-means Algorithm
Sowan, B. I., Matar, N. A., Omar, F., Alauthman, M., & Eshtay, M. (2020) · International Arab Conference on Information Technology (ACIT) · doi:10.1109/acit50332.2020.9300084 · Cited by 1
“The well-known example that adopting this task is a market basket analysis for exploring consumers behavior [16-18].”
BackgroundJoshi, S., Dubey, M., & Mishra, D. K. (2021) · Journal of University of Shanghai for Science and Technology · doi:10.51201/jusst12653 · Cited by 1
“The development of the internet has things exerting significant influence in several areas including smart city [3], smart healthcare[4], intelligent transport[5], air-link [6], data mining [7], manufacturing [8], and environmental monitoring [9-13].”
BackgroundFusion of probabilistic unreliable indirect information into estimation serving to decision making
Kárný, M., & Hůla, F. (2021) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-021-01359-9 · Cited by 1
A comprehensive study of data intelligence in the context of big data analytics
Banchhor, C. B., & Srinivasu, N. (2022) · Web Intelligence · doi:10.3233/web-210480 · Cited by 1
“Most of the data mining approaches include various steps such as input, output, initialization, data scan, rule construction, and update operators [78] as indicated in the Algorithm 1.”
Industry 4.0 as the Last Industrial Revolution and Its Opportunities for Developing Countries
HOPALI, E., & Vayvay, Ö. (2021) · IGI Global eBooks · doi:10.4018/978-1-7998-8548-1.ch056 · Cited by 1
The Dynamics of the Ubiquitous Internet of Things (IoT) and Trailblazing Data Mining (DM)
Lainjo, B. (2022) · International Journal of Wireless & Mobile Networks · doi:10.5121/ijwmn.2022.14302 · Cited by 1
“Handheld devices, such as mobile phones, have made access to ambiguous information processing more feasible [25].”
BackgroundAn Expeditious kNN Algorithm for Massive IoT Data Classification
Jacob, S. S., & Mohamed, A. (2022) · International Conference on Computing, Communication, Security and Intelligent Systems (IC3SIS) · doi:10.1109/ic3sis54991.2022.9885354 · Cited by 1
“The characteristics of data generated by IoT and the analysis and summarization of some of the mining technologies for IoT is given in [9].”
Mining Algorithm for Eliminating Redundant Data in WSN
Hemavathi, S., & Latha, B. (2022) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc54411.2022.9885344 · Cited by 1
“A study by Tsai et al. [17] examined the current data mining methodologies for IoT settings and demonstrated pattern detection from smart home applications.”
BackgroundDESIGN AND IMPLEMENTATION OF AN IOTBASED AUTOMATED AGRICULTURAL MONITORING AND CONTROL SYSTEM
(2022) · International Research Journal of Modernization in Engineering Technology and Science · doi:10.56726/irjmets30126 · Cited by 1
Webar Application For An Air Quality System In IOT-Based Smart Environement
Badouch, A., & Krit, S.-D. (2023) · International Journal of Membrane Science and Technology · doi:10.15379/ijmst.v10i3.1509 · Cited by 1
“The energy data is displayed on the screen, overlapping the image of the recognized object [34].”
BackgroundSelf-Organizing Logical-Clustering Topology for Managing Distributed Context Information
Rahman, H. (2015) · KTH Publication Database DiVA (KTH Royal Institute of Technology) · Cited by 1
Atat, R. (2017) · KU ScholarWorks (The University of Kansas) · Cited by 1
Extracting knowledge from raw IoT data streams
Akbar, A. (2018) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1
Enterprise, project and workforce selection models for industry 4.0.
Kaur, R. (2018) · Spectrum Research Repository (Concordia University) · Cited by 1
An Introduction to RFID SystemsPreprint
Ahmed, M. R. (2022) · doi:10.36227/techrxiv.20438616.v1 · Cited by 1
Lewson, S. (2015) · doi:10.15368/theses.2015.75
“” Essentially, the idea of the Internet of Things is to connect all “things” to the internet, allowing them to communicate and make decisions[39] in order to collaborate to perform actions and achieve goals[27].”
BackgroundA Review on Current Recommendation Systems
Sharma, S., & Dhir, M. (2015) · International Journal of Computer Applications · doi:10.5120/ijca2015907500
Research and Implement on Intelligent Scheduling of Coal Ash Delivering
Zhang, N., Zhang, Y., Yu, W., & Xu, L. (2016) · International Journal of u- and e- Service Science and Technology · doi:10.14257/ijunesst.2016.9.6.29
Park, H., Han, M., & Choi, Y. (2016) · 정보과학회 컴퓨팅의 실제 논문지 · doi:10.5626/ktcp.2016.22.7.320
Mapas Auto-Organizados aplicados a la segmentación de clientes en entornos empresariales
Figueroa-Diaz, R., Fernanda., S., Guamán-Quinché, R., & Cueva, M. (2016) · DOAJ (DOAJ: Directory of Open Access Journals)
Enhanced Swarm based Optimized Recommendation System
Sharma, S. (2016) · International Journal of Computer Applications · doi:10.5120/ijca2016911720
Knowledge Mining from Medical Images
Ashour, A. S., Dey, N., & Satapathy, S. C. (2017) · CRC Press eBooks · doi:10.1201/9781315399744-11
IR-UWB RFID Circuits and Systems
Lee, K. K. (2014) · NORA - Norwegian Open Research Archives · doi:10.13140/rg.2.1.4435.9840
Umasundari, R., & Dr.Chidambaram, M. (2017) · International Journal of Engineering and Technology · doi:10.21817/ijet/2017/v9i3/170903078
“In this implementation made by Shelly Gupta, Dharminder Kumar and Anand Sharma et al.[1] described on highest classification accuracy and least error rate over the healthcare datasets.”
MethodThe Role of Autonomous Computing, Cloud Computing, and Multimedia in IoT
Mostafa, A. M. (2017) · Advances in wireless technologies and telecommunication book series · doi:10.4018/978-1-5225-2799-2.ch005
La asignatura “Internet de las Cosas” en el master DATCOM de la UGR
Damas, M. (2017) · Enseñanza y Aprendizaje de Ingeniería de Computadores · doi:10.30827/digibug.47375
“enfermedades cardiovasculares), dar recomendaciones, reconocer patrones o coincidencias (por ejemplo para ayudar a los clientes de un supermercado a encontrar los productos que están buscando), también para descubrir comportamientos o detectar anomalías en el funcionamiento de máquinas industriales, entre otras cosas,…”
BackgroundKnowledge Discovery in Power System Based on Spatial Data Mining
Wu, Q., Wang, Y., Liu, F., & Xia, Y. (2017) · International Conference on Multimedia and Image Processing (ICMIP) · doi:10.1109/icmip.2017.8
“The details can be found in literature [11].”
BackgroundInfluentialSupporting IoT Data Similarity at the Edge Towards Enabling Distributed Clustering
Rahman, H. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-319-77703-0_21
“Clustering is an efficient way of exploring data where each cluster is a group of data which exhibits similar characteristics and dissimilar from other groups of data [14].”
BackgroundA Novel Design of Sophisticated Distributed Knowledge Extraction Process on Grid Architecture
Parveen, S., & Narsimha, G. (2018) · Communications on Applied Electronics · doi:10.5120/cae2018652740
“Hence, performing analytical operation of such forms of data itself possess a computational challenge irrespective of number of research-based approaches on mining [4]-[7].”
BackgroundDecision Tree Incremental Learning Algorithm Oriented Intelligence Data
Wang, H. (2018) · International Journal of Performability Engineering · doi:10.23940/ijpe.18.05.p3.849856
Special Issue on Advanced Learning Methods for Complex Data
Atzori, M., & Pes, B. (2018) · Information · doi:10.3390/info10010008
Social Internet of Mobile Things and Decision Support Tools
Wachowicz, M., Cha, S., & Renso, C. (2018) · Springer eBooks · doi:10.1007/978-1-4939-7131-2_327
Deep learning and Big Data Analysis: Challenges, Opportunities and Applications
Banday, M. (2018) · International Journal of Trend in Scientific Research and Development · doi:10.31142/ijtsrd12710
“information( KDD), is the subfield of software engineering, which isinterdisciplinary and is the computational procedure of finding designs in expansive indexes of information ("enormous information") which includes techniques at the convergence of computerized reasoning, machine learning, measurements, and frameworks…”
BackgroundTowards Smart Data-oriented Services
Ahonen, T., Hanski, J., Uusitalo, T., Jännes, J., Hyvärinen, M., Vainio, H., Kunttu, S., Valkokari, P., Kortelainen, H., & Koskinen, K. T. (2017)
DEW 2019: Data Exploration in the Web 3.0 Age
Atzori, M., & Pes, B. (2019) · IEEE International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE) · doi:10.1109/wetice.2019.00037
“A growing attention is thus devoted to the design of suitable approaches for exploring this kind of data [4, 5], in order to extract actionable knowledge about people, things, and their interactions.”
BackgroundModeling Internet of Things Data for Knowledge Discovery
Shafi, M., Shah, S. Z. A., & Amjad, M. (2019) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-32150-5_45
Machine Learning in 5G Multimedia Communications
Milovanovic, D. A., Bojkovic, Z. S., & Kukolj, D. (2020) · Advances in computational intelligence and robotics book series · doi:10.4018/978-1-5225-9643-1.ch017
Machine Learning-Assisted Cognition of Driving Context and Avoidance of Road Obstacles
Hina, M. D., Ortalda, A., Soukane, A., & Ramdane-Chérif, A. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-49559-6_7
A Reliable Resource Scheduling Approach with Dataflow Natural Attribute Priority
Fang, Y., Chen, Q., & Wang, J. (2020)
Classification Method of Indoor Environmental Data based on Internet of Things
Fan, J., Zhu, H., & Wang, X. (2020) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls49620.2020.9275275
“Tsai [5] systematically described traditional data mining algorithms (k-means, decision trees, etc.) and discussed in detail how to associate them with IoT, providing guidance for those focused on integrating data mining with IoT to move to the next generation of the Internet of Things.”
BackgroundMixture ratio modeling of dynamic systems
Kárný, M., & Ruman, M. (2021) · International Journal of Adaptive Control and Signal Processing · doi:10.1002/acs.3219
“This allows us to consider the joint parametric pd in (10) as the finite weighted sum of EF components, pds (Jc(Ψ|Θc))c∈c on Ψ, J(Ψ|Θ) ≡ ∑ c∈c αcJc(Ψ|Θc) ≡ ∑ c∈c αc exp ⟨Bc(Ψ),Cc(Θc)⟩, c ≡ {1, .”
BackgroundReverse Clustering—The Essence and The Interpretations
Owsiński, J. W., Stańczak, J. T., Opara, K. R., Zadrożny, S., & Kacprzyk, J. (2021) · Studies in computational intelligence · doi:10.1007/978-3-030-69359-6_2
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Almahjoub, A., & Qiu, D. (2021) · WSEAS Transactions on Circuits and Systems archive · doi:10.37394/23201.2021.20.10
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Jain, H., & Shrivastava, R. (2021) · International Journal of Engineering and Applied Physics
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Liang, X., Wang, R., & Li, H. (2021) · Journal of Nanoelectronics and Optoelectronics · doi:10.1166/jno.2021.3073
Human-Centered Modeling Applications In Intelligent Manufacturing Systems
Guillén, I., & Montalvo, F. L. (2021) · Proceedings of the Human Factors and Ergonomics Society Annual Meeting · doi:10.1177/1071181321651292
“Furthermore, using data mining methods (Tsai et al., 2013), coupled with machine learning (Wuest et al.”
MethodMultidimensional Survey of Machine Learning Application in IoT (Internet of Things)
Sinha, A., & Jha, P. (2021) · Human Communication & Technology · doi:10.1002/9781119752165.ch11
Hecho imponible y concepto de actividad económica en el IAE
Gea, F. G.-F. (1998) · Revista de hacienda local
A Comprehensive Assessment on IOT Devices with Data Mining Techniques
Kumar, G. V., Sreedevi, M., Yadav, A., & Aruna, B. (2021) · Advances in parallel computing · doi:10.3233/apc210211
“They should settle on choice without help from anyone else or follow the human orders [4].”
BackgroundInfluentialMachine learning prediction of the electronic property of binary transition metal alloysPreprint
Che, Y., Zhao, J., & Wang, H. (2021) · ChemRxiv · doi:10.26434/chemrxiv-2021-wl3p6
Data Mining Problems Classification and Techniques
Rahman, N. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-2460-2.ch006
Major Big Data Challenges in Most Industries and Innovative Solutions
(2019) · International Journal of Innovative Technology and Exploring Engineering · doi:10.35940/ijitee.b1143.1292s219
Lee, I. (2019) · IGI Global eBooks · doi:10.4018/978-1-7998-0945-6.ch091
Lin, X., Ni, J., & Shen, X. (2018) · Springer briefs in electrical and computer engineering · doi:10.1007/978-3-030-02113-9_1
Machine Learning in Wireless Communication
Sharma, N. V., & Yadav, N. S. (2020) · IGI Global eBooks · doi:10.4018/978-1-7998-7705-9.ch088
Big Data Analysis and Implementation in Different Areas Using IoT
Rehman, A. U., Fahad, M., Ullah, R., & Abdullah, F. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-9866-4.ch049
Rehman, A.-U., Ullah, R., & Abdullah, F. (2015) · Advances in data mining and database management book series · doi:10.4018/978-1-4666-8505-5.ch015
Architecting IoT based Healthcare Systems Using Machine Learning Algorithms
Karthick, G. S., & Pankajavalli, P. B. (2022) · IGI Global eBooks · doi:10.4018/978-1-6684-6291-1.ch012
IoT Protocol Security Mechanisms
Karthika, D., & Kalaiselvi, K. (2022) · doi:10.1002/9781119896746.ch3
Frequent pattern mining algorithms in fog computing environments: A systematic review
Tehrani, A. F., Sharifi, M., & Rahmani, A. M. (2022) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.7229
Advancement of Deep Learning and Blockchain Technology in Health Informatics
Singh, A., Sankhla, M. S., Parihar, K., & Kumar, R. (2022) · Wiley eBooks · doi:10.1002/9781119792734.ch11
An Introduction to RFID SystemsPreprint
Ahmed, M. R. (2022) · doi:10.36227/techrxiv.20438616
Multiple Access Towards Non-terrestrial Networks
Ye, N., Li, X., Yang, K., & An, J. (2022) · Springer eBooks · doi:10.1007/978-981-19-4025-5_3
Social Internet of Mobile Things and Decision Support Tools
Wachowicz, M., Cha, S., & Renso, C. (2017) · Springer eBooks · doi:10.1007/978-1-4614-7163-9_327-1
Framework of Crop Yield Using pH Soil and Temperature Values
Murthy, B. V. R., & Chandrika, B. N. S. M. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-4990-6_53
Rbox: A web API for software integration with the R programming language
Konomos, A., & Chountasis, S. (2023) · Computer Applications in Engineering Education · doi:10.1002/cae.22621
“Today's technological progress combined with the rapid growth of the internet has revealed the need for new forms of data analysis [8, 21, 37].”
BackgroundA Machine Learning-Based Framework for Intrusion Detection Systems in Healthcare Systems
Pant, J., Sharma, R. K., Pant, H., Singh, D., & Pant, D. (2023) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-6684-6646-9.ch006
Data Analysis and Visualization of the Industrial IoT Based on the Big Data Approach
Ganesan, R., Roy, C., Ramiya, R., Hemambujavalli, S., & Vijayakumar, R. (2023) · International Conference on Electronics, Communication and Aerospace Technology (ICECA) · doi:10.1109/iceca58529.2023.10395206
“The research by [9] aims to answer some common questions about data preparation and processing, considered the biggest challenges in the areas of IoT and data analysis.”
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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