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 101–200 of 618
Parthasarathy, P., & Vivekanandan, S. (2018) · International Journal of Computers and Applications · doi:10.1080/1206212x.2018.1457471 · Cited by 92
Khediri, S. E., Guiloufi, A. B., Thaljaoui, A., Moulahi, T., Khan, R. U., Alabdulatif, A., & Lorenz, P. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101892 · Cited by 105
“The authors of Tsai et al. (2013) used SVD-based numerical analysis to find missing information in a picture.”
InfluentialHome automation networks: A survey
Toschi, G. M., Campos, L. B., & Cugnasca, C. E. (2016) · Computer Standards & Interfaces · doi:10.1016/j.csi.2016.08.008 · Cited by 91
“This table was based on the comparison tables presented on [19, 72, 73, 74].”
MethodCuomo, S., Michele, P. D., Piccialli, F., Galletti, A., & Jung, J. E. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.02.034 · Cited by 85
García, G. C., Ruiz, I. L., & Gómez‐Nieto, M. Á. (2016) · Sensors · doi:10.3390/s16111968 · Cited by 86
Internet of Thing (IoT) review of review: Bibliometric overview since its foundation
Sadeghi‐Niaraki, A. (2023) · Future Generation Computer Systems · doi:10.1016/j.future.2023.01.016 · Cited by 93
Stolpe, M. (2016) · ACM SIGKDD Explorations Newsletter · doi:10.1145/2980765.2980768 · Cited by 86
“[100] present existing applications of well-known data analysis algorithms in an IoT context, highlighting decentralized data analysis as open issue concerning infrastructure.”
MethodApplication of Internet of Things in logistics – current challenges
Tadejko, P. (2015) · Ekonomia i Zarządzanie · doi:10.12846/j.em.2015.04.07 · Cited by 80
Popa, D., Pop, F., Serbanescu, C., & Castiglione, A. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3724-6 · Cited by 82
RetractedA Survey of Emerging M2M Systems: Context, Task, and Objective
Cao, Y., Jiang, T., & Han, Z. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2582540 · Cited by 80
“When the volume of data, the variety of data types, and the velocity of data generated are massive, it is quite challenging to make analysis for the “big data” [40], [41].”
BackgroundLi, Y., Orgerie, A., Ivan Rodero, A., Amersho, B. L., Parashar, M., & Menaud, J.-M. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.12.048 · Cited by 80
“The generated data can represent huge volumes that have to be processed in real time [43].”
BackgroundLi, Z., Wang, J., Higgs, R. J., Zhou, L., & Yuan, W. (2017) · IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC) · doi:10.1109/cse-euc.2017.212 · Cited by 78
“(Middleware is a kind of software that needs to be installed on a specific computer [17] [18].”
BackgroundZaidan, A. A., Zaidan, B. B., Alsalem, M. A., Albahri, O. S., Albahri, A. S., & Qahtan, M. Y. (2019) · Neural Computing and Applications · doi:10.1007/s00521-019-04325-3 · Cited by 78
A review on big data based parallel and distributed approaches of pattern mining
Kumar, S., & Mohbey, K. K. (2019) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2019.09.006 · Cited by 80
“(Chen et al., 1996; Anastasiu et al., 2014; Aggarwal et al., 2009; Tsai et al. (2013).”
Internet of Behaviors: A Survey
Sun, J., Gan, W., Chao, H., Yu, P. S., & Ding, W. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3247594 · Cited by 78
“Data mining will run through the entire IoB data process to achieve decision making, control, and prediction [79].”
MethodDeep Learning Challenges and Prospects in Wireless Sensor Network Deployment
Qiu, Y., Ma, L., & Priyadarshi, R. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-024-10079-6 · Cited by 83
REDPC: A residual error-based density peak clustering algorithm
Parmar, M. D., Wang, D., Zhang, X., Tan, A., Miao, C., Jiang, J., & Zhou, Y. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.06.087 · Cited by 76
Industry 4.1 for Wheel Machining Automation
Cheng, F., Tieng, H., Yang, H., Hung, M., Lin, Y., Wei, C.-F., & Shieh, Z.-Y. (2016) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2016.2517208 · Cited by 73
“In fact, CM is also regarded as one of the best solutions for implementing IoT/CPS [3], [6], [8], [10], [11], [16], [17], [18] because of its powerful computing capability.”
Metaheuristic Algorithms for Healthcare: Open Issues and Challenges
Tsai, C., Chiang, M., Ksentini, A., & Chen, M. (2016) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2016.03.005 · Cited by 71
Self-citationOlasupo, T. O., Otero, C. E., Olasupo, K. O., & Kostanic, I. N. (2016) · IEEE Transactions on Antennas and Propagation · doi:10.1109/tap.2016.2583507 · Cited by 67
“[12] discuss the characterization of wireless communication in hard court arenas used in sports,”
BackgroundMethodSingh, A., Payal, A., & Bharti, S. (2019) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2019.06.013 · Cited by 66
Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial
Kulin, M., Fortuna, C., Poorter, E. D., Deschrijver, D., & Moerman, I. (2016) · Sensors · doi:10.3390/s16060790 · Cited by 63
“• Data-driven research has found applications in various schemes of wireless networks [13], diverse fields of communication networks [6–8] and different fields of science in general [2].”
BackgroundBloom filter based optimization scheme for massive data handling in IoT environment
Singh, A., Garg, S., Batra, S., Kumar, N., & Rodrigues, J. J. P. C. (2017) · Future Generation Computer Systems · doi:10.1016/j.future.2017.12.016 · Cited by 63
Deep Learning Based Single Carrier Communications Over Time-Varying Underwater Acoustic Channel
Zhang, Y., Li, J., Zakharov, Y. V., Li, J., Li, Y., Lin, C., & Li, X. (2019) · IEEE Access · doi:10.1109/access.2019.2906424 · Cited by 59
“In particular, deep learning (DL) techniques feature great potential for solving nonparamet ric problems such as object detection and recognition, voice recognition, and object tracking [17]–[19], [21].”
BackgroundCloud based SDN and NFV architectures for IoT infrastructure
Alenezi, M., Almustafa, K. M., & Meerja, K. A. (2018) · Egyptian Informatics Journal · doi:10.1016/j.eij.2018.03.004 · Cited by 59
Predictive maintenance: strategic use of IT in manufacturing organizations
March, S. T., & Scudder, G. D. (2017) · Information Systems Frontiers · doi:10.1007/s10796-017-9749-z · Cited by 58
“The Internet of Things (IoT) has significant strategic potential for improving organizational performance in a variety of industries (Gubbi et al. 2013; Manyika et al. 2015; Tsai et al. 2014; Wang et al. 2015).”
BackgroundCost-sensitive stacked auto-encoders for intrusion detection in the Internet of Things
Telikani, A., & Gandomi, A. H. (2019) · Internet of Things · doi:10.1016/j.iot.2019.100122 · Cited by 55
The Hitchhiker’s Guide to Fused Twins: A Review of Access to Digital Twins In Situ in Smart Cities
Grübel, J., Thrash, T., Aguilar, L., Gath-Morad, M., Chatain, J., Sumner, R. W., Hölscher, C., & Schinazi, V. R. (2022) · Remote Sensing · doi:10.3390/rs14133095 · Cited by 57
A Hybrid Case Based Reasoning Model for Classification in Internet of Things (IoT) Environment
Biswas, S. K., Devi, D., & Chakraborty, M. (2018) · Journal of Organizational and End User Computing · doi:10.4018/joeuc.2018100107 · Cited by 53
K-means tree: an optimal clustering tree for unsupervised learning
Tavallali, P., Tavallali, P., & Singhal, M. (2020) · The Journal of Supercomputing · doi:10.1007/s11227-020-03436-2 · Cited by 51
A knowledge discovery in databases approach for industrial microgrid planning
Gamarra, C., Guerrero, J. M., & Montero, E. A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.01.091 · Cited by 52
“DM technologies are reviewed, described and classified in [8] into clustering, classification, and frequent patterns mining technologies, from the perspective of infrastructures and from the perspective of services by C.”
BackgroundGiri, A., Dutta, S., Neogy, S., Dahal, K. P., & Pervez, Z. (2017) · doi:10.1145/3109761.3109768 · Cited by 52
“A comprehensive survey on IoT data mining technique including classification, clustering, pattern mining and knowledge discovery in database (KDD) is done in [118].”
BackgroundIndoor navigation systems based on data mining techniques in internet of things: a survey
Sattarian, M., Rezazadeh, J., Farahbakhsh, R., & Bagheri, A. (2018) · Wireless Networks · doi:10.1007/s11276-018-1766-4 · Cited by 58
“Scalability Scalability of an INS means the system ensures the normal positioning function when it scales in one of two dimensions: geography and number of users, indicates that the number of units located per time period per geographic area increases Location-Aware Communications for 5G Networks [84], BLE profile ada…”
MethodIoT Data Compression and Optimization Techniques in Cloud Storage
Hossain, K., Rahman, M. M., & Roy, S. (2019) · International Journal of Cloud Applications and Computing · doi:10.4018/ijcac.2019040103 · Cited by 51
IoT Paradigm into the Smart City Vision: A Survey
Medina, C. A., Pérez, M., & Arboleda, L. C. (2017) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings-greencom-cpscom-smartdata.2017.109 · Cited by 51
“Undoubtedly, this process significantly complements the IoT vision especially in a Smart City, because as stated in [32] is necessary to convert the data captured by IoT devices into knowledge to perform statistical studies, decision making and extract real value of the collected data.”
BackgroundInternet of Things and data mining: From applications to techniques and systems
Gaber, M., Aneiba, A., Basurra, S., Batty, O., Elmisery, A. M., Kovalchuk, Y., & Rehman, M. H. U. (2018) · WIREs Data Mining Knowl. Discov. · doi:10.1002/widm.1292 · Cited by 45
Hsu, A., Khoo, W., Goyal, N., & Wainstein, M. E. (2020) · Frontiers in Big Data · doi:10.3389/fdata.2020.00029 · Cited by 56
“In the context of IoT, Tsai et al. (2014b) argue that a future digital ecosystem will not only involve making “things” smart and scaling up the internet infrastructure to handle the exponential growth in connected devices, but also integrating data mining to provide better services through new…”
BackgroundInfluentialInternet of things: a survey of challenges and issues
Sarhan, Q. I. (2018) · International Journal of Internet of Things and Cyber-Assurance · doi:10.1504/ijitca.2018.090162 · Cited by 50
Sanín, C., Zhang, H., Shafiq, S. I., Waris, M. M., de Oliveira, C. S., & Szczerbicki, E. (2018) · Future Generation Computer Systems · doi:10.1016/j.future.2018.01.062 · Cited by 52
Cluster-based information retrieval using pattern mining
Djenouri, Y., Belhadi, A., Djenouri, D., & Lin, J. C. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01922-x · Cited by 51
Evaluating Data Mining Classification Methods Performance in Internet of Things Applications
Sulaiman, M. A. (2020) · International Journal of Sustainable Construction Engineering and Technology (Universiti Tun Hussein Onn Malaysia) · Cited by 47
Rehman, M. H. U., Liew, C. S., Wah, T. Y., & Khan, M. K. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.11.031 · Cited by 48
“For example, the authors in Gaber et al. (2005), Parthasarathy et al. (2007), Goel et al. (2010), Fuqiang (2011), Krishnaswamy et al. (2012), Tsai et al. (2014), Gaber et al. (2014a), Nguyen et al. (2015) and Chen et al. (2015) focused on general MDSM algorithms and lack the discussion on…”
Chen, C., Qiu, T., Hu, J., Ren, Z., Zhou, Y., & Sangaiah, A. K. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.12.014 · Cited by 48
“In the recent decades, to improve the road safety, traffic efficiency and travel comfort, the Intelligent Transportation System (ITS) has become a very hot topic, which is actually a system consisting of heterogeneous networks such as IoT[1, 2], LTE, VANETs, GPS, GIS, sensor network[3, 4] etc.”
BackgroundExploring the Applications of Machine Learning in Healthcare
Saleem, T. J., & Chishti, M. A. (2019) · International Journal of Sensors Wireless Communications and Control · doi:10.2174/2210327910666191220103417 · Cited by 47
An IoT‐based monitoring approach for cultural heritage sites: The Matera case
Gribaudo, M., Iacono, M., & Levis, A. H. (2017) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.4153 · Cited by 47
Anomaly Detection in Cyber-Physical Systems Using Machine Learning
Rouzbahani, H. M., Karimipour, H., Rahimnejad, A., Dehghantanha, A., & Srivastava, G. (2020) · Springer eBooks · doi:10.1007/978-3-030-38557-6_10 · Cited by 47
Desingh, V., & Baskaran, R. (2021) · The International Journal of Health Planning and Management · doi:10.1002/hpm.3331 · Cited by 51
Advancements in energy harvesting techniques for sustainable IoT devices
Ali, A., Shaukat, H., Elahi, H., Taimur, S., Manan, M. Q., Altabey, W. A., Kouritem, S. A., & Noori, M. N. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104820 · Cited by 46
Recent Advances in the Internet of Things: Multiple Perspectives
Wang, F., Hu, L. Q., Hu, J., Zhou, J., & Zhao, K. (2016) · IETE Technical Review · doi:10.1080/02564602.2016.1155419 · Cited by 45
“[48] reviewed the features of data from the IoT and applied the concept of data mining to it.”
MethodSEIRA: An effective algorithm for IoT resource allocation problem
Tsai, C. (2017) · Computer Communications · doi:10.1016/j.comcom.2017.10.006 · Cited by 45
Self-citationInternet of Things: A Review on Technologies, Architecture, Challenges, Applications, Future Trends
Mudhar, J. K., & Kaur, K. (2017) · International Journal of Computer Network and Information Security · doi:10.5815/ijcnis.2017.04.07 · Cited by 44
A Fast Density-Based Clustering Algorithm for Real-Time Internet of Things Stream
Amini, A., Saboohi, H., Wah, T. Y., & Herawan, T. (2014) · The Scientific World JOURNAL · doi:10.1155/2014/926020 · Cited by 43
Green IoT: Advancements and Sustainability with Environment by 2050
Sharma, N., & Panwar, D. (2020) · International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) · doi:10.1109/icrito48877.2020.9197796 · Cited by 43
“Sensors are working fine only when Data Centers fulfill their demands [15] [16] and need high processing and analysis which consumes large amount of energy resources and damaging eco system [17] and to solve this issue “Green IoT” comes into picture.”
BackgroundMa, M., Lin, W., Pan, D., Lin, Y., Wang, P., Zhou, Y., & Liang, X. (2017) · Journal of Signal Processing Systems · doi:10.1007/s11265-017-1304-0 · Cited by 42
“aged such as data mining techniques [5], complex event processing [6], etc.”
BackgroundSavaglio, C., Gerace, P., Fatta, G. D., & Fortino, G. (2019) · International Conference on Computer Communications and Networks (ICCCN) · doi:10.1109/icccn.2019.8846941 · Cited by 42
“In particular, the (narrow) state-of-the-art about Data Mining and IoT consists in (i) studies to assess whether well-known Data Mining techniques are suitable for the IoT context or novel classes of algorithms are required [7] [3] [8] [9] [10]; (ii) surveys on new multi-layer architectural models for IoT Data Mining…”
MethodAn Empirical Comparison of Supervised Machine Learning Algorithms for Internet of Things Data
Khadse, V. M., Mahalle, P. N., & Biraris, S. V. (2018) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea.2018.8697476 · Cited by 41
“In [10] authors propose a survey to counter the challenges faced in preparing and preprocessing of data in IoT by using data mining techniques, this study divided the research into three major parts.”
MethodEfficient and Privacy-Preserving Decision Tree Classification for Health Monitoring Systems
Liang, J., Qin, Z., Xue, L., Lin, X., & Shen, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3066307 · Cited by 44
“Coupled with recent advances of wearable devices and mobile communication networks [3], health monitoring systems work as follows: a hospital first utilizes the decision tree classification technique to produce a clinical decision model, and later tests clients’ biomedical data collected from…”
BackgroundMethodA review on non-terrestrial wireless technologies for Smart City Internet of Things
Wang, A., Wang, P., Miao, X., Li, X., Ye, N., & Liu, Y. (2020) · International Journal of Distributed Sensor Networks · doi:10.1177/1550147720936824 · Cited by 39
Cognitive decision making in smart industry
Kaur, N., & Sood, S. K. (2015) · Computers in Industry · doi:10.1016/j.compind.2015.06.006 · Cited by 39
“[42] summarized the various data mining approaches which can be used in IoT environment.”
BackgroundZhang, P., Liu, Y., Wu, F., Liu, S., & Tang, B. (2016) · IEEE Communications Letters · doi:10.1109/lcomm.2016.2521735 · Cited by 38
“N OWADAYS, the 6LoWPAN [1] and Constrained Application Protocol (CoAP) [2] facilitate massive sensors to instantaneously publish structured sensor data to the Internet of Things (IoT) [3], [4], which promote the content-based sensor search to be a promising service in the IoT.”
Internet of Things and data analytics: A current review
Mohindru, G., Mondal, K., & Banka, H. (2019) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.1341 · Cited by 38
“The IoT application framework should support different kinds of queries depending on the application requirements (Tsai et al., 2013): 1.”
BackgroundHan, J., Lee, G. H., Park, S., Lee, J., & Choi, J. K. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3124673 · Cited by 38
IoT data analytic algorithms on edge-cloud infrastructure: A review
Edje, A. E., Latiff, M. S. A., & Chan, W. H. (2023) · Digital Communications and Networks · doi:10.1016/j.dcan.2023.10.002 · Cited by 43
A parallel metaheuristic data clustering framework for cloud
Tsai, C., Liu, S.-J., & Wang, Y. (2017) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2017.10.020 · Cited by 35
“In [64], we presented a unified framework to describe the mining algorithms, as shown in Algorithm 1.”
BackgroundMethodInfluentialSelf-citationKey technologies of ubiquitous power Internet of Things-aided smart grid
Jiang, A., Yuan, H., Li, D., & Tian, J. (2019) · Journal of Renewable and Sustainable Energy · doi:10.1063/1.5121856 · Cited by 35
Utility-Driven Data Analytics on Uncertain Data
Gan, W., Lin, J. C., Chao, H., Vasilakos, A. V., & Yu, P. S. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.2979279 · Cited by 35
“management, and decision-making [2], [3], [12].”
How Machine Learning Accelerates the Development of Quantum Dots? †
Peng, J., Ramzan, M. A., Wang, S., & Zhong, H. (2020) · Chinese Journal of Chemistry · doi:10.1002/cjoc.202000393 · Cited by 35
A review of feature set partitioning methods for multi-view ensemble learning
Kumar, A., & Yadav, J. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101959 · Cited by 34
A Game Theoretic Approach for an IoT-Based Automated Employee Performance Evaluation
Kaur, N., & Sood, S. K. (2015) · IEEE Systems Journal · doi:10.1109/jsyst.2015.2469102 · Cited by 34
“[11] can be used to infer useful results about the collected data.”
BackgroundData Analytics in the Internet of Things: A Survey
Saleem, T. J., & Chishti, M. A. (2019) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v20i4.1562 · Cited by 34
Privacy-Preserving Tensor-Based Multiple Clusterings on Cloud for Industrial IoT
Zhao, Y., Yang, L. T., & Sun, J. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2871174 · Cited by 32
Self-citationThe Effect of “Internet of Things” on Aircraft Spare Parts Inventory Management
Keivanpour, S., & Aı̈t-Kadi, D. (2019) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2019.11.556 · Cited by 32
An effective handling of secure data stream in IoT
Jang, J., Jung, I. Y., & Park, J. H. (2017) · Applied Soft Computing · doi:10.1016/j.asoc.2017.05.020 · Cited by 31
Caching transient data in Information-Centric Internet-of-Things (IC-IoT) networks: A survey
Al-Ward, H., Tan, C. K., & Lim, W. H. (2022) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2022.103491 · Cited by 30
Marino, F., Moiso, C., & Petracca, M. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.11.011 · Cited by 30
EC-CENTRIC: An Energy- and Context-Centric Perspective on IoT Systems and Protocol Design
Biason, A., Pielli, C., Rossi, M., Zanella, A., Zordan, D., Kelly, M. Y., & Zorzi, M. (2017) · IEEE Access · doi:10.1109/access.2017.2692522 · Cited by 29
“Novelty: our approach develops along the same lines of [8], [10]–[12]; we seek a data processing algorithm that self-adapts to the properties of the signal that is being measured, i.”
BackgroundLou, H. (2020) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.2020040102 · Cited by 29
A wireless multi-channel low-cost lab-on-chip algae culture monitor AIoT system for algae farm
Liao, Y., Yu, N., Zhou, G., Wu, Y., & Wang, C. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106647 · Cited by 31
An analysis of critical factors for adopting machine learning in manufacturing supply chains
Gardas, R., & Narwane, S. V. (2023) · Decision Analytics Journal · doi:10.1016/j.dajour.2023.100377 · Cited by 38
Learning Based Anomaly Detection in Critical Cyber-Physical Systems
Mozaffari, F. S., Karimipour, H., & Parizi, R. M. (2020) · Springer eBooks · doi:10.1007/978-3-030-45541-5_6 · Cited by 29
Ding, Y., Huang, L., Ji, X., & Ma, M. (2024) · International Journal of Biological Macromolecules · doi:10.1016/j.ijbiomac.2024.138106 · Cited by 28
Towards internet of things modeling: a gateway approach
Altamimi, A. B., & Ramadan, R. Α. (2016) · Complex Adaptive Systems Modeling · doi:10.1186/s40294-016-0038-3 · Cited by 28
Abushakra, A., & Nikbin, D. (2019) · International Conference on Knowledge Management in Organizations · doi:10.1007/978-3-030-21451-7_29 · Cited by 27
Evaluating temporal aggregation for predicting the sea surface temperature of the Atlantic Ocean
Salles, R. P., Mattos, P., Iorgulescu, A.-M. D., Bezerra, E. A. D., de Lima, L., & Ogasawara, E. (2016) · Ecological Informatics · doi:10.1016/j.ecoinf.2016.10.004 · Cited by 28
“to communication interferences and instruments/sensors malfunction [4, 9, 10, 39, 56].”
Big data and algorithmic trading against periodic and tangible asset reporting: The need for U-XBRL
Pei, D., & Vasarhelyi, M. A. (2020) · International Journal of Accounting Information Systems · doi:10.1016/j.accinf.2020.100453 · Cited by 27
Ahirwar, M. K., Shukla, P. K., & Singhai, R. (2021) · Scientific Programming · doi:10.1155/2021/8715668 · Cited by 26
Miniaturized humidity and temperature sensing RFID enabled tags
Satti, J. A., Habib, A., Anam, H., Zeb, S., Amin, Y., Loo, J. K. K., & Tenhunen, H. (2017) · International Journal of RF and Microwave Computer-Aided Engineering · doi:10.1002/mmce.21151 · Cited by 26
“It ensures connectivity of physical and virtual objects to the internet at an unprecedented rate, thus, transforming traditional objects into smart ones.(1,2) This technology is an integral part of internet future, and it will interconnect uniquely identified embedded items through ubiquitous communication of physical…”
BackgroundInternet of Things: An Implementation and Its Challenges in Malaysia
Badarudin, U. W., Din, W. I. S. W., Prasetyo, Y. A., Musa, Z. B., & Kasim, S. B. (2018) · International Journal on Advanced Science Engineering and Information Technology · doi:10.18517/ijaseit.8.6.5043 · Cited by 26
“The evolution of cell phone usage also a significant factor in the evolution of the Internet of Things [19] driven by the need to communicate anywhere and at any time.”
BackgroundRecommender Systems in Cybersecurity
Ferreira, L., Silva, D. C., & Uriarte, M. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-01906-6 · Cited by 31
“Tsai et al. [120] inspected the data mining application for IoT.”
Cross-platform machine learning characterization for task allocation in IoT ecosystems
Cui, W., Kim, Y., & Rosing, T. (2017) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc.2017.7868438 · Cited by 26
“The proposed framework also considers network communication cost and increasing processing demand.”
BackgroundYouTube Video Ranking by Aspect-Based Sentiment Analysis on User Feedback
Chauhan, G. S., & Meena, Y. K. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-981-13-3600-3_6 · Cited by 25
Exploring technology opportunities and evolution of IoT-related logistics services with text mining
Chen, M., & Ho, P. H. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00453-3 · Cited by 25
Cruz-Ramos, C., Garcia-Salgado, B. P., Reyes-Reyes, R., Ponomaryov, V. I., & Sadovnychiy, S. (2021) · Remote Sensing · doi:10.3390/rs13152914 · Cited by 25
Improved Neighboring Reference Points Selection Method for Wi-Fi Based Indoor Localization
Xue, W., Hua, X., Li, Q., Yu, K., & Qiu, W. (2018) · IEEE Sensors Letters · doi:10.1109/lsens.2018.2818161 · Cited by 25
“The number of clusters has a great impact on the performance of k-means method [14], [15], but the proposed algorithm does not involve any selection Fig.”
MethodProfit optimization in service-oriented data market: A Stackelberg game approach
Shen, B., Shen, Y., & Ji, W. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2018.12.072 · Cited by 26
Chhowa, T. T., Rahman, M. A., Paul, A. K., & Ahmmed, R. (2019) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecace.2019.8679200 · Cited by 27
Trajectory Planning of UAV in Wireless Powered IoT System Based on Deep Reinforcement Learning
Zhang, J., Yu, Y., Wang, Z., Ao, S., Tang, J., Zhang, X. Y., & Wong, K. (2020) · IEEE/CIC International Conference on Communications in China (ICCC) · doi:10.1109/iccc49849.2020.9238842 · Cited by 25
“, event monitoring and predication for health safety [2] [3], is restricted by the freshness of information.”
BackgroundAnomaly Detection and Redundancy Elimination of Big Sensor Data in Internet of ThingsPreprint
Xie, S., & Chen, Z. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.03225 · Cited by 25
“It is expected that things can be identified automatically, can communicate with each other, and even can make decisions by themselves [1].”
BackgroundThe Convergence of Machine Learning and CommunicationsPreprint
Samek, W., Stańczak, S., & Wiegand, T. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1708.08299 · Cited by 25
“Moreover, machine learning methods are core part in many emerging applications of communication technology, e.g., smart cities [47] or internet of things [46].”
Node Level Energy Efficiency Protocol for Internet of Things
Vellanki, M., & Kandukuri, S. (2015) · Journal of Theoretical and Computational Science · doi:10.4172/2376-130x.1000140 · Cited by 24
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
