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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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Zadtootaghaj, P., Mohammadian, A., Mahbanooei, B., & Ghasemi, R. (2019) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.22059/jitm.2019.288695.2398 · Cited by 12
“…Jamalian & Ghasemi , 2017; Atzori et al., 2014; Chui et al., 2010; Da Xu et al., 2014; Gubbi et al., 2013; He et al., 2014; Jara et al., 2011a; Luo et al., 2010; Miorandi et al., 2012; Perera et al., 2014; Tsai et al., 2014; Vermesan et al., 2013; Xu, 2002; Zarei et al., 2016; Zheng et al., 2011).”
Innovation in Energy Systems - New Technologies for Changing Paradigms
Ustun, T. S. (2019) · IntechOpen eBooks · doi:10.5772/intechopen.81310 · Cited by 11
Analysis on data mining models for Internet Of Things
Singh, A., & Sharma, S. B. (2017) · International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) · doi:10.1109/i-smac.2017.8058313 · Cited by 11
“[22]”
Efficient event prediction in an IOT environment based on LDA model and support vector machine
Dami, S., & Yahaghizadeh, M. (2018) · Iranian Joint Congress on Fuzzy and Intelligent Systems · doi:10.1109/cfis.2018.8336655 · Cited by 11
“Data mining can be very useful in intelligent systems to provide faster services [7].”
BackgroundData analytics for internet of things: A review
Tsai, C., Tsai, P., Chiang, M., & Yang, C. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1261 · Cited by 11
“…data analytics architecture presented in Section 3.1 is based on many recent studies (Ferreira et al., 2013; Ganchev et al., 2014; Hu, 2015; Tsai et al., 2014; Wan et al., 2016) in which the data analytics system was developed to analyze the data of IoT, we will turn our discussion to…”
BackgroundMethodInfluentialSelf-citationSmart Cities: A Worldwide Journey into Intelligent Urbanism and State-of-the-Art Technologies
Sharma, H., & Kanwal, N. (2023) · Scientific and Technical Information Processing · doi:10.3103/s0147688223040081 · Cited by 14
“A comprehensive investigation performed by [206] in the field of data mining (DM) revealed that huge amounts of data gathered by numerous sensors and devices surrounding consumers are utilized to mine new laws and information to deliver better services.”
A scientometric analysis of reviews on the Internet of Things
Jain, S., Sukul, P., Groppe, J., Warnke, B., Harde, P., -, R. J., Rehan, W., Sehgelmeble, Y. C., Fischer, S., & Groppe, S. (2025) · The Journal of Supercomputing · doi:10.1007/s11227-025-07230-w · Cited by 10
Detecting Incremental Frequent Subgraph Patterns in IoT Environments
Bok, K., Jeong, J., Choi, D., & Yoo, J. (2018) · Sensors · doi:10.3390/s18114020 · Cited by 10
“In the IoT environment, frequent subgraph pattern is used for analyzing interactions among various objects or for determining anomalies [33–35].”
BackgroundPerformance Analysis of Data Mining Techniques in IoT
Batra, I., Verma, S., & Janjua, K. (2018) · International Conference on Computing Sciences (ICCS) · doi:10.1109/iccs.2018.00039 · Cited by 10
Zhou, M., Zhang, H., Lv, T., Huang, W. K., Duan, Y., & Gao, Y. (2024) · Frontiers in Marine Science · doi:10.3389/fmars.2023.1331635 · Cited by 10
A Stream Processing System for Multisource Heterogeneous Sensor Data
Hu, L. Q., Sun, R., Wang, F., Fei, X., & Zhao, K. (2016) · Journal of Sensors · doi:10.1155/2016/4287834 · Cited by 10
“Authors in [23] make an investigation on data mining for IoT.”
BackgroundHybrid short term prediction to address limited timeliness of public transport data streams
Grzenda, M., Kwasiborska, K., & Zaremba, T. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.08.100 · Cited by 10
Industrie 4.0: Agile Entwicklung und Produktion im Internet of Production
Schuh, G., Riesener, M., Prote, J.-P., Dölle, C., Molitor, M., Schloesser, S., Liu, Y., & Tittel, J. (2020) · Springer eBooks · doi:10.1007/978-3-662-58474-3_24 · Cited by 10
A path generation scheme for real-time green internet of things
Lu, Y.-F., Wu, J., & Kuo, C. (2014) · ACM SIGAPP Applied Computing Review · doi:10.1145/2656864.2656868 · Cited by 10
“He joined the Department of Computer Science and Information Engineering (CSIE), National University of Kaohsiung (NUK), Kaohsiung, Taiwan, R.O.C. as an Assistant Professor in 2006.”
BackgroundA Multiple-Search Multi-Start Framework for Metaheuristics for Clustering Problems
Hu, K.-C., Tsai, C., & Chiang, M. (2020) · IEEE Access · doi:10.1109/access.2020.2994813 · Cited by 10
“Metaheuristics [1], [2] have been developed for over half a century and have been successfully applied to a wide range of fields to solve difficult optimization problems, such as scheduling problems in a cloud computing environment [3], [4] and data mining problems for the internet of things [5], [6].”
BackgroundSelf-citationGui, J., & Qun-qi, W. (2018) · Journal of Advanced Transportation · doi:10.1155/2018/4360516 · Cited by 9
“[12], and cloud computing security [13].”
Decentralized knowledge discovery using massive heterogenous data in Cognitive IoT
Jha, V., & Tripathi, P. (2023) · Cluster Computing · doi:10.1007/s10586-023-04154-z · Cited by 9
“These unprocessed data either belong to the administrator, i.e., information about devices and networks vital to infrastructure, or the information is a part of the CIoT built environment parameters data set [9], i.e., the data includes sensor readings, actuator responses, and more.”
Mageswari, U., Deepak, G., Santhanavijayan, A., & Mala, C. (2024) · International Journal of Information Technology · doi:10.1007/s41870-024-01800-6 · Cited by 9
“Elephants often form intricate social groups, such as those led by a female elephant with her calves or other closely related females, which are referred to as clans [30].”
Inductive reasoning for significant concept and pattern discovery in cognitive IoT
Jha, V., & Tripathi, P. (2024) · Service Oriented Computing and Applications · doi:10.1007/s11761-024-00416-9 · Cited by 9
Denoising Latent Representation with SOMs for Unsupervised IoT Malware Detection
Nguyen, H. N., Tran, N. N., Hoang, T.-H., & Cao, V. L. (2022) · SN Computer Science · doi:10.1007/s42979-022-01344-1 · Cited by 9
“Machine learning (ML) has been widely used to develop solutions for many security problems like detecting malicious codes, identifying and classifying network attacks, and recognizing anomalous behaviors of network data [5–8].”
BackgroundA distributed software-defined multi-agent architecture for unifying IoT applications
Liu, X., Leon‐Garcia, A., & Zhu, P. (2017) · IEEE Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON) · doi:10.1109/iemcon.2017.8117142 · Cited by 9
“The likelihood of applying the data mining [19-21], context-aware computing [22-24], agent theories [28-30] and software-defining [25-27] into IoT is explored.”
BackgroundExtended vertical lists for temporal pattern mining from multivariate time series
Андреевич, К. А., Momčilović, P., Bihorac, A., & Pardalos, P. M. (2019) · Expert Systems · doi:10.1111/exsy.12448 · Cited by 9
Linked Data Processing for Human-in-the-Loop in Cyber–Physical Systems
Zheng, Z., Mumtaz, S., Khosravi, M. R., & Menon, V. G. (2021) · IEEE Transactions on Computational Social Systems · doi:10.1109/tcss.2020.3029569 · Cited by 9
“By using all these technologies, we can try to uncover the hidden patterns, unknown correlations, and other useful information [9], [10].”
BackgroundUnderwater acoustic OFDM communications using deep learning
Zhang, Y., Li, J., Zakharov, Y. V., Sun, D.-J., & Li, J. (2018) · ePrints Soton (University of Southampton) · Cited by 9
Impact of the Internet of Things on Psychology: A Survey
Vahdat‐Nejad, H., Mansoor, W., Abbasi, S., Hajiabadi, M., Salmani, F., Azizi, F., Mosafer, R., Jamalian, M., & Farsani, H. K. (2022) · Smart Cities · doi:10.3390/smartcities5030060 · Cited by 9
“The resultant information is used to make predictions, make decisions, and smartify systems [88].”
BackgroundDistributed live data search architecture for resource discovery on Internet of Things
Ikebe, T., Noguchi, H., & Hoshikawa, N. (2016) · IEEE World Forum on Internet of Things (WF-IoT) · doi:10.1109/wf-iot.2016.7845394 · Cited by 8
An On-Device Cognitive Dynamic Systems Inspired Sensing Framework for the IoT
Pérez-Torres, R., Torres-Huitzil, C., & Galeana‐Zapién, H. (2018) · IEEE Communications Magazine · doi:10.1109/mcom.2018.1700224 · Cited by 8
“Processing such real-world data toward decision making and actuation in the environment is an outstanding opportunity and one of the biggest challenges for this new ecosystem [1, 2].”
BackgroundFIDOOP: PARALLEL MINING OF FREQUENT ITEM SETS USING MAPREDUCE
Kavitha, K. (2017) · International Journal of Advanced Research in Computer Science · doi:10.26483/ijarcs.v8i7.4408 · Cited by 8
“[5] surveyed the data mining for the internet of thing.”
BackgroundSocial media analytics and internet of things
Ayele, W. Y., & Juell-Skielse, G. (2017) · doi:10.1145/3109761.3158379 · Cited by 8
“Also, Tsai et al. 2014 surveyed about data mining for Internet of Things and reviewed “data from IoT” and “data mining for IoT” [58].”
BackgroundNotice of Retraction: Enabling Hardware Green Internet of Things: A review of Substantial Issues
Alsharif, M. H., Kelechi, A. H., Kim, S., Khan, I., Kim, J. G., & Kim, J. H. (2019) · IEEE Access · doi:10.1109/access.2019.2926800 · Cited by 8
“To process these enormous sensor payload, huge processing and analytics capabilities are needed which in turn, consumes huge energy resources [7, 8].”
IoT and Fog Enabled Model for Saffron Cultivation in Precision Farming
Kour, K., Gupta, D., & Gupta, K. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725737 · Cited by 8
“The use of IoT along with data mining to provide information to users is discussed in [45].”
Threats and vulnerabilities of wireless sensor networks in smart cities
Obaidat, M. A. (2022) · Elsevier eBooks · doi:10.1016/b978-0-12-817784-6.00024-2 · Cited by 8
Deep learning and metaheuristics application in internet of things: A literature review
Khelili, M. A., Slatnia, S., Kazar, O., Merizig, A., & Mirjalili, S. (2023) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2023.104792 · Cited by 8
An N-List-Based Approach for Mining Frequent Inter-Transaction Patterns
Nguyen, T.-N., Nguyen, L. T. T., Vo, B., Nguyên, N. T., & Nguyen, T. D. D. (2020) · IEEE Access · doi:10.1109/access.2020.3004530 · Cited by 8
“Many researchers have thus focused on investigating efficient data-mining solutions for IoT systems [1], [2].”
BackgroundPapageorgiou, A. S., Cheng, B., & Kovács, E. (2015) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2015.89 · Cited by 7
“Time series stemming from IoT devices are one of the main enablers of Big Data [9], [12].”
BackgroundEfficient Task-Offloading in IoT-Fog Based Health Monitoring System
Gupta, A., & Chaurasiya, V. K. (2022) · OITS International Conference on Information Technology (OCIT) · doi:10.1109/ocit56763.2022.00098 · Cited by 7
“Therefore, the BBN classifier is an excellent choice to give the most appropriate output with the help of condition joint probability for uncertainty events [12].”
BackgroundData mining applications in risk research: A systematic literature review
Sıcakyüz, Ç., Edalatpanah, S. A., & Pamucar, D. S. (2024) · International Journal of Knowledge-based and Intelligent Engineering Systems · doi:10.1177/13272314241296866 · Cited by 12
Applying Decisional DNA to Internet of Things: The Concept and Initial Case Study
Zhang, H., Sanín, C., & Szczerbicki, E. (2015) · Cybernetics & Systems · doi:10.1080/01969722.2015.1007738 · Cited by 7
A Research Direction on Data Mining with IOT
Chovatiya, F., Prajapati, P., Vasa, J., & Patel, J. K. (2017) · Smart innovation, systems and technologies · doi:10.1007/978-3-319-63673-3_22 · Cited by 7
Knowledge discovery for enabling smart Internet of Things: A survey
Misra, S., Mukherjee, A., & Roy, A. (2018) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1276 · Cited by 7
A Survey Report on Deep Learning Techniques in Underwater Acoustic Channels
Das, A. K., & Pramanik, A. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-2449-3_35 · Cited by 7
Predicting Round-Trip Time Distributions in IoT Systems using Histogram Estimators
Flinta, C., Yan, W., & Johnsson, A. (2020) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms47738.2020.9110315 · Cited by 7
“A more extensive review of other predictive techniques for IoT is available in [14].”
BackgroundA Reduced Network Traffic Method for IoT Data Clustering
Azevedo, R. D., Machado, G. R., Goldschmidt, R. R., & Choren, R. (2020) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3423139 · Cited by 7
“By and large, two main data mining approaches can be used to analyze IoT data [34, 40]: distributed or centralized.”
BackgroundMethodFeature Representation of AutoEncoders for Unsupervised IoT Malware Detection
Nguyen, H. N., Nguyen, V. C., Tran, N. N., & Cao, V. L. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-91387-8_18 · Cited by 7
Automated Detection of Electric Energy Consumption Load Profile Patterns
Benítez, I., & Díez, J.-L. (2022) · Energies · doi:10.3390/en15062176 · Cited by 7
Borges, J. B., Ramos, H. S., & Loureiro, A. A. F. (2022) · ACM Transactions on Internet of Things · doi:10.1145/3533049 · Cited by 7
“1 INTRODUCTION The classiication of time series is a fundamental research topic that has gained a new breath in the last years, mainly due to the increasing needs from big data and Internet of Things (IoT) communities in better understanding the data they are dealing with [32, 49, 54].”
BackgroundZschech, P. (2022) · Information Systems and e-Business Management · doi:10.1007/s10257-022-00577-0 · Cited by 7
“…inspired by the procedure model of Nickerson et al. (2013) and chose to define a tripartite meta-characteristic covering analysis objectives , data characteristics , and analysis methods to distinguish between output, input, and throughput dimensions of data analytics solutions (Tsai et al. 2014).”
MethodUncertainty modeling and applications for operating data-driven inverse design
Li, S., Hou, L., Chen, Z., Wang, S., & Bu, X. (2023) · Journal of Engineering Design · doi:10.1080/09544828.2023.2180846 · Cited by 7
“Threemain factors are considered in these processes: the purpose of data mining, the data characteristics, and the algorithms used (Tsai et al. 2014).”
BackgroundFinite State Automata on Multi-Word Units for Efficient Text-Mining
Postiglione, A. (2024) · Mathematics · doi:10.3390/math12040506 · Cited by 10
Auto-configuration System and Algorithms for Big Data-Enabled Internet-of-Things Platforms
Papageorgiou, A. S., Zahn, M., & Kovács, E. (2014) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdata.congress.2014.78 · Cited by 7
“Keywords-M2M; IoT; configuration; gateway; autonomic; self-management; I. INTRODUCTION The Internet of Things (IoT) is expected to be one of the most important sources for Big Data [1], [2].”
BackgroundIndustrial Guidelines for Stimulating Entrepreneurship with the Internet of Things
Zarei, M., Jamalian, A., & Ghasemi, R. (2017) · Advances in e-business research series · doi:10.4018/978-1-5225-2104-4.ch008 · Cited by 7
Data Logging and Visualization Using Bolt IoT
Prachchhak, G., Bhatt, C., & Thik, J. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-2673-8_18 · Cited by 7
Metric Indexing for Efficient Data Access in the Internet of Things
Beecks, C., Graß, A., & Devasya, S. (2018) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata.2018.8622387 · Cited by 7
“[7] and Chen et al.”
Predictive Maintenance System for Efficiency Improvement of Heating Equipment
Santiago, A. R., Antunes, M., Barraca, J. P., Gomes, D., & Aguiar, R. L. (2019) · IEEE International Conference on Big Data Computing Service and Applications (BigDataService) · doi:10.1109/bigdataservice.2019.00019 · Cited by 7
“Data Mining works as a knowledge acquisition tool of the databases [13] and can be used to find hidden information on IoT data [14] such as time-series.”
BackgroundArchitecting IoT based Healthcare Systems Using Machine Learning Algorithms
Karthick, G. S., & Pankajavalli, P. B. (2019) · Advances in medical technologies and clinical practice book series · doi:10.4018/978-1-7998-1090-2.ch003 · Cited by 7
Data formating and visualization of BIM and sensor data in building management systems
Ignatov, I. I., & Gade, P. N. (2019) · Cited by 7
Evolution of Automation and Development Strategy of Intelligent Manufacturing with Zero Defects
Cheng, F. (2021) · doi:10.1002/9781119739920.ch1 · Cited by 7
Sufyan, S. N., Mohammed, Y. A. Y. A., Mu, C., Bashir, K., Wen, N., Mamoor, M., Jiao, X., Chen, D., & Wang, T. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5c07407 · Cited by 6
Mining Sequential Update Summarization with Hierarchical Text Analysis
Zhang, C., Si, Z., Ma, Z., Xi, X., & Yin, Y. (2016) · Mobile Information Systems · doi:10.1155/2016/1340973 · Cited by 6
“One of the key techniques is to develop semantic and intelligent web for IoT [4].”
MethodStudying the performance of clustering techniques for biomedical data using spark
Shobanadevi, A., & Maragatham, G. (2017) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2017.8389249 · Cited by 6
“Data analysis techniques using massive data sets to develop potential applications could be found in each and every human activity domain [1][2].”
MethodA survey of data mining methodologies in the environment of IoT and its variants
Marshoodulla, S. Z., & Saha, G. K. (2024) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2024.103907 · Cited by 8
Jung, S.-H., Lee, H., & Huh, J. (2020) · Entropy · doi:10.3390/e22080902 · Cited by 6
“There have been active e ff orts to create a new industry or increase the quality of medicine with data accumulated in the field of healthcare around the globe, and these e ff orts are contributing as important elements in the era of Smart Revolution [1–3].”
BackgroundChallenges and Opportunities in Secure Smart Cities for Enhancing the Security and Privacy
Saini, S., Chauhan, A., Thakur, G., & Sapra, L. (2023) · Springer eBooks · doi:10.1007/978-3-031-22922-0_1 · Cited by 6
Han, J., Lee, G. H., Lee, J., & Choi, J. K. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3316829 · Cited by 6
Industry 4.0 as the Last Industrial Revolution and Its Opportunities for Developing Countries
HOPALI, E., & Vayvay, Ö. (2018) · Advances in business information systems and analytics book series · doi:10.4018/978-1-5225-3468-6.ch004 · Cited by 6
Alharthi, A., Lipscomb, M., & Tanik, M. M. (2019) · IEEE SoutheastCon · doi:10.1109/southeastcon42311.2019.9020487 · Cited by 6
Towards Argumentation-based Recommendations for Personalised Patient Empowerment
Fernández, J. M., Mamei, M., Mariani, S., Miralles, F., Steblin, A., Vargiu, E., & Zambonelli, F. (2017) · IRIS UNIMORE (University of Modena and Reggio Emilia) · Cited by 6
An energy-aware data cleaning workflow for real-time stream processing in the internet of things
Oliveira, E. A. R. D., Delicato, F. C., & Mattoso, M. (2020) · Anais do Workshop de Computação Urbana (CoUrb) · doi:10.5753/courb.2020.12354 · Cited by 6
“Buffering incoming data on a predefined length or time interval to create frames (windows) is a common approach to perform operations on data streams [Tsai et al. 2014].”
BackgroundMethodMir, M. H., Jamwal, S., Islam, S., & Khan, Q. R. (2021) · International Conference on Advances in Computing, Communication Control and Networking (ICAC3N) · doi:10.1109/icac3n53548.2021.9725648 · Cited by 6
“Data mining is a technique for uncovering hidden patterns in order to gain new insights[9].”
MethodData-driven predictive modeling of Hubble parameter
Saltı, M., Ciger, E., Kangal, E. E., & Zengin, B. (2022) · Physica Scripta · doi:10.1088/1402-4896/ac807c · Cited by 6
Ko, H. S., & Eshraghi, S. (2023) · Springer handbooks · doi:10.1007/978-3-030-96729-1_61 · Cited by 6
Industrial Internet of Things with Data Distribution Service for Real Time Systems
Ungurean, I. (2015) · Cited by 6
An Exploratory Study of the Impact of the Internet of Things (IoT) on Business Model Innovation
Lee, I. (2017) · The Internet of Things · doi:10.4018/978-1-5225-1832-7.ch020 · Cited by 6
Zobaed, S., & Salehi, M. A. (2018) · Encyclopedia of Big Data Technologies · doi:10.1007/978-3-319-63962-8_40-1 · Cited by 6
“mining technologies offer solutions for analysis of big data generated by IoT systems (Tsai et al. 2014).”
BackgroundDimension Reduction Methods for Collaborative Mobile Gossip Learning
Berta, Á., Hegedűs, I., & Jelasity, M. (2016) · Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) · doi:10.1109/pdp.2016.20 · Cited by 5
“This model covers a wide range of applications including smart metering [1], collaborative mobile platforms [2] and Internet of Things platforms [3].”
BackgroundContext Management Life Cycle for Internet of Things: Tools, Techniques, and Open Issues
Vijayvargia, K., Saxena, P., & Bhilare, D. S. (2025) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.9117 · Cited by 6
“It enables applications to perform automatic identification of objects, facilitate communication, and make autonomous decisions [1].”
Towards IPv6-based Architecture for Big Data Processing of Community Medical Internet of Things
Liu, C., Zhang, C., Chen, F., & Zhao, C. (2018) · International Wireless Communications and Mobile Computing Conference (IWCMC) · doi:10.1109/iwcmc.2018.8450268 · Cited by 5
“Compared with the PCA method, PR technology aims to reduce the number of data patterns in the process of data aggregation and not to reduce the data dimension [13].”
MethodGeneration and verification of learned stochastic automata using k-NN and statistical model checking
Baouya, A., Chehida, S., Ouchani, S., Bensalem, S., & Bozga, M. D. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02884-4 · Cited by 5
“For instance, [61] provided a survey on how data mining algorithms (clustering and classification) can be employed in data extraction, processing and analysis, and consequently knowledge.”
BackgroundKnowledge Discovery and Data Mining for Intelligent Business Solutions
Pushp, & Chand, S. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-16-5689-7_18 · Cited by 5
The Role of IoT Technologies in Malaysia During the Covid-19 Pandemic: A Mini-Revie
Muhamad, N. A., Zahid, F. S., Othman, N., & Sin, N. D. M. (2023) · Jurnal Kejuruteraan · doi:10.17576/jkukm-2023-35(3)-06 · Cited by 5
“Emerging technologies such as Machine learning ( ML ), Big Data analytics, the Internet of Things, and blockchain technology were the most used technologies (Akpan et al. 2021).”
BackgroundInfluentialSmart Grid Fault Detection and Classification Framework Utilizing AIoT in India
Avasthi, S., Sanwal, T., & Verma, S. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-0968-1.ch011 · Cited by 5
Mihailović, A., Simeunović, M., Lekić, N., & Pejanović-Djurišić, M. (2014) · Telecommunications Forum (TELFOR) · doi:10.1109/telfor.2014.7034349 · Cited by 5
Grid-PPPS: A Skyline Method for Efficiently Handling Top- k Queries in Internet of Things
Ihm, S.-Y., Nasridinov, A., & Park, Y. (2014) · Journal of Applied Mathematics · doi:10.1155/2014/401618 · Cited by 5
“[12] surveyed research on how to connect data mining technologies to the IoT, which include clustering, classification, and frequent patterns mining technologies, from a different perspective.”
BackgroundData and Decision Intelligence for Internet of Things: Putting Human in the Loop
Ma, M., Lin, W., Pan, D., Wang, P., Zhou, Y., & Liang, X. (2017) · IEEE International Conference on Big Data Security on Cloud, IEEE International Conference on High Performance and Smart Computing, and IEEE International Conference on Intelligent Data and Security (BigDataSecurity/HPSC/IDS) · doi:10.1109/bigdatasecurity.2017.49 · Cited by 5
Scenario-based Vulnerability Analysis in IoT-based Patient Monitoring System
Mekki, N., Hamdi, M., Aguili, T., & Kim, T.-H. (2017) · International Joint Conference on e-Business and Telecommunications (ICETE) · doi:10.5220/0006473005540559 · Cited by 5
Analysis of Direction on Product Design in the Era of the Internet of Things
Tian, B., Yu, S., Chu, J., & Li, W. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201817601002 · Cited by 5
O paradigma tecnológico da Internet das coisas e sua relação com a Ciência da Informação
Rozsa, V., Dutra, M. L., Pinto, A. L., & Muriel-Torrado, E. (2017) · Informação & Sociedade Estudos · doi:10.22478/ufpb.1809-4783.2017v27n3.36975 · Cited by 5
Data Mining Problems Classification and Techniques
Rahman, N. (2018) · International Journal of Big Data and Analytics in Healthcare · doi:10.4018/ijbdah.2018010104 · Cited by 5
Measuring Performances of a White-Box Approach in the IoT Context
Albricci, D. G. V., Ceria, M., Cioschi, F., Fornari, N., Shakiba, A., & Visconti, A. (2019) · Symmetry · doi:10.3390/sym11081000 · Cited by 5
“Data collected by IoT devices need to be (a) processed to form informations by applying, for example, data mining techniques [4]; (b) evaluated in order to make decision by adopting agent based models [5,6], bayesian decision models [7], fuzzy logic [8] and so on; (c) protected from attacks,…”
MethodSyu, J.-H., Yeh, Y.-R., Wu, M., & Ho, J.-M. (2021) · Mathematics · doi:10.3390/math9101093 · Cited by 5
“With the dramatically growth of information and computing power, researchers pay close attention to the topics of big data and data mining, which have been widely-used in various fields, including business [5,6], finance [7], and engineering [8], and Internet of Things (IoT) [9,10].”
BackgroundMining Evidences of Internet of Robotic Things (IoRT) Software from Open Source Projects
Albonico, M., Rohling, A. J., Santos, J. L. S., & Varela, P. J. (2021) · Brazilian Symposium on Software Components, Architectures, and Reuse (SBCARS) · doi:10.1145/3483899.3483900 · Cited by 5
Matlab-based Graphical User Interface for IoT Sensor Measurements Subject to Outlier
Bashir, A. F., Awawdeh, M., Faisal, T., & Queen, M. P. F. (2022) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset53988.2022.9735063 · Cited by 5
“In order to develop an efficient data mining module for IoT-based data, three main characteristics have to be considered [11].”
BackgroundA Survey of Big Data Analytics Using Machine Learning Algorithms
Moorthy, U., & Gandhi, U. D. (2021) · IGI Global eBooks · doi:10.4018/978-1-6684-3662-2.ch031 · Cited by 6
ML Model Optimization–Selection and GFA Prediction for Binary Alloys
Pan, J., Xiao, J., Tian, Z., Hu, Y., & Li, K. (2022) · Crystal Growth & Design · doi:10.1021/acs.cgd.1c01519 · Cited by 5
Kumar, S., & Raza, Z. (2018) · IGI Global eBooks · doi:10.4018/978-1-5225-5649-7.ch001 · Cited by 5
A Survey of Big Data Analytics Using Machine Learning Algorithms
Moorthy, U., & Gandhi, U. D. (2017) · Advances in human and social aspects of technology book series · doi:10.4018/978-1-5225-2863-0.ch005 · Cited by 5
Candidate Technologies and Evaluation Challenges for 5G
Yang, Y., Xu, J., Shi, G., & Wang, C. (2017) · Wireless networks · doi:10.1007/978-3-319-61869-2_1 · Cited by 5
Lee, I. (2019) · IGI Global eBooks · doi:10.4018/978-1-5225-9866-4.ch071 · Cited by 5
Internet of Things and anomaly detection for the iron ore mining industry
Saroufim, C. E. (2016) · Cited by 5
Interpretable Success Prediction in a Computer Networks Curricular Unit Using Machine Learning
de Oliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.06.212 · Cited by 4
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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