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.
“Data mining and machine learning approaches have been used for IoT data processing and analysis [52], [53].”
From Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things (2021) · cites this paper as [52]
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Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents
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Monitoring and Modeling Simple Everyday Activities of the Elderly at Home
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A video-based door monitoring system using local appearance-based face models
Ekenel, H. K., Stallkamp, J., & Stiefelhagen, R. (2010) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2009.06.009 · Cited by 25 · see citing works
Real-Time Human Tracker Based on Location and Motion Recognition of User for Smart Home
Nguyen, Q. C., Shin, D., Shin, D., & Kim, J. (2009) · International Conference on Multimedia and Ubiquitous Engineering (MUE) · doi:10.1109/mue.2009.51 · Cited by 24 · see citing works
A Framework for Outlier Mining in RFID data
Masciari, E. (2007) · Proceedings - International Database Engineering and Applications Symposium · doi:10.1109/ideas.2007.4318112 · Cited by 23 · see citing works
Real time heart ischemia detection in the smart home care system
Pang, L., Tchoudovski, I., Bolz, A., Braecklein, M., Egorouchkina, K., & Kellermann, W. (2005) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2005.1617286 · Cited by 23 · see citing works
An intelligent driver guidance tool using location based services
Perera, K., & Días, D. (2011) · IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM) · doi:10.1109/icsdm.2011.5969041 · Cited by 22 · see citing works
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TMS-RFID: Temporal management of large-scale RFID applications
Li, X., Liu, J., Sheng, Q. Z., Zeadally, S., & Zhong, W. (2009) · Information Systems Frontiers · doi:10.1007/s10796-009-9211-y · Cited by 21 · see citing works
On clusterization of "big data" streams
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Real-Time Automatic ECG Diagnosis Method Dedicated to Pervasive Cardiac Care
Zhou, H., Hou, K.-M., & Zuo, D. (2009) · Wireless Sensor Network · doi:10.4236/wsn.2009.14034 · Cited by 16 · see citing works
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A Wavelet-Based Pattern Recognition Algorithm to Classify Postural Transitions in Humans
Fleury, A., Noury, N., & Vacher, M. (2009) · HAL (Le Centre pour la Communication Scientifique Directe) · Cited by 15 · see citing works
An Investigation into printing industry trends
Romano, F. (2004) · RIT Scholar Works (Rochester Institute of Technology) · Cited by 13 · see citing works
Keller, T. (2011) · Alexandria (UniSG) (University of St.Gallen) · Cited by 13 · see citing works
Application Study of Precision Agriculture Based on Ontology in the Internet of Things Environment
Zhang, M., Bei, W., Chunyuan, G., & qian, S. Z. (2011) · Communications in computer and information science · doi:10.1007/978-3-642-23226-8_49 · Cited by 12 · see citing works
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Simulation and analysis of buying behavior in supermarkets
Schwenke, C., Vasyutynskyy, V., & Kabitzsch, K. (2010) · IEEE Conference on Emerging Technologies & Factory Automation (ETFA) · doi:10.1109/etfa.2010.5641255 · Cited by 10 · see citing works
Prometheus — Fuzzy information retrieval for semantic homes and environments
Portmann, E., Andrushevich, A., Kistler, R., & Klapproth, A. (2010) · Conference on Human System Interactions · doi:10.1109/hsi.2010.5514482 · Cited by 10 · see citing works
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Sound Processing for Health Smart Home
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Smart Home Sensor Networks Pose Goal-Driven Solutions to Wireless Vacuum Systems
Chen, H., Cheng, B., Cheng, C.-C., & Tsai, L.-K. (2006) · doi:10.1109/ichit.2006.219 · Cited by 7 · see citing works
A plant-and-play wireless sensor network system for gate monitoring
Sudhaakar, R. S., Sanzgiri, A., Demirbaş, M., & Qiao, C. (2009) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom.2009.5282488 · Cited by 7 · see citing works
An Intelligent RFID System for Consumer Businesses
Somasundaram, S., Khandavilli, P., & Sampalli, S. (2010) · doi:10.1109/greencom-cpscom.2010.134 · Cited by 7 · see citing works
User-pattern analysis framework to predict future service in intelligent home network
Kim, H. S., & Son, J. H. (2009) · doi:10.1109/icicisys.2009.5358266 · Cited by 7 · see citing works
Tree-Based Classification Algorithm for Heterogeneous Unique Item ID Schemes
Lee, Y. H., Kim, H. J., Roh, B., Yoo, S. W., & Oh, Y. C. (2005) · Lecture notes in computer science · doi:10.1007/11596042_110 · Cited by 6 · see citing works
Automatic sensor clustering : connectivity for the internet of things
Kardeby, V. (2011) · Diva portal (Dalarna University Library) · Cited by 6 · see citing works
Ubiquitous Intelligent Sensing System for a Smart Home
Choi, J., Shin, D., & Shin, D. (2006) · Lecture notes in computer science · doi:10.1007/11815921_35 · Cited by 5 · see citing works
A Distributed Data Mining System for a Novel Ubiquitous Healthcare Framework
Viswanathan, M. K., Whangbo, T. K., Lee, K. J., & Yang, Y.-K. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72588-6_117 · Cited by 5 · see citing works
Time To Live: Temporal Management of Large-Scale RFID Applications
Li, X., Liu, J., Sheng, Q. Z., & Zhong, W. (2008) · Cited by 4 · see citing works
Association of Data Mining and healthcare domain: Issues and current state of the art
Bekri, F. E., & Govardhan, A. (2011) · Global Journal of Computer Science and Technology · Cited by 4 · see citing works
Smart Home Sensor Networks Pose Goal-Driven Solutions to Wireless Vacuum Systems
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Chen, L., & Ren, G. (2012) · Communications in computer and information science · doi:10.1007/978-3-642-32427-7_67 · Cited by 3 · see citing works
Flagged first, then by citations
4 of the 156 references couldn’t be matched to a known paper.
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Sorted by citations
Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2444095 · Cited by 8,630 · see citing works
“tion, dimensionality reduction, feature selection, and distributed computing methods [166].”
BackgroundMethodLin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2683200 · Cited by 2,793 · see citing works
“…is enacted to sense and collect the status data of objects in IoT, data aggregation integrates an amount of related data into a comprehensive information, and data mining and analytics extract the potential value of integrated comprehensive information for special applications in IoT [125].”
BackgroundDeep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,847 · see citing works
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security
Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2020.2988293 · Cited by 1,392 · see citing works
“insights from these data, researchers [51]–[57] have discussed different methods of integrating big data analytical methods with IoT design.”
BackgroundMethodDeep Learning for IoT Big Data and Streaming Analytics: A Survey
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2018.2844341 · Cited by 1,471 · see citing works
Machine learning in materials science
Wei, J., Chu, X., Sun, X., Xu, K., Deng, H., Chen, J., Wei, Z., & Lei, M. (2019) · InfoMat · doi:10.1002/inf2.12028 · Cited by 1,069 · see citing works
Machine Learning for Internet of Things Data Analysis: A Survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P. M., & Sheth, A. (2017) · Surrey Research Insight Open Access (The University of Surrey) · Cited by 1,008 · see citing works
Network Intrusion Detection for IoT Security Based on Learning Techniques
Chaabouni, N., Mosbah, M., Zemmari, A., Sauvignac, C., & Faruki, P. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2896380 · Cited by 945 · see citing works
“[21] survey both, features of data for IoT and features for data mining for IoT with a discussion about changes, potentials, open issues, and future trends.”
BackgroundMachine learning for internet of things data analysis: a survey
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018) · Digital Communications and Networks · doi:10.1016/j.dcan.2017.10.002 · Cited by 921 · see citing works
“[12] ran a survey to respond to some of the challenges in preparing and processing data on the IoT through data mining techniques.”
BackgroundA survey of machine learning for big data processing
Qiu, J., Wu, Q., Ding, G., Xu, Y., & Feng, S. (2016) · EURASIP Journal on Advances in Signal Processing · doi:10.1186/s13634-016-0355-x · Cited by 925 · see citing works
“In [9], the authors reviewed studies on applying data mining and KDD technologies to the IoT.”
BackgroundInternet of Things (IoT): A review of enabling technologies, challenges, and open research issues
Čolaković, A., & Hadžialić, M. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.07.017 · Cited by 825 · see citing works
Tsai, C.-W., Lai, C.-F., Chao, H.-C., & Vasilakos, A. V. (2015) · Journal of Big Data · doi:10.1186/s40537-015-0030-3 · Cited by 818 · see citing works
“4 shows, most data mining algorithms contain the initialization, data input and output, data scan, rules construction, and rules update operators [26].”
BackgroundSelf-citationZhang, J., & Tao, D. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3039359 · Cited by 774 · see citing works
“Data mining and machine learning approaches have been used for IoT data processing and analysis [52], [53].”
MethodDíáz, M., Martín, C., & Rubio, B. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.01.010 · Cited by 689 · see citing works
Survey on blockchain for Internet of Things
Wang, X., Zha, X., Ni, W., Liu, R. P., Guo, Y. J., Niu, X., & Zheng, K. (2019) · Computer Communications · doi:10.1016/j.comcom.2019.01.006 · Cited by 567 · see citing works
The Emerging Internet of Things Marketplace From an Industrial Perspective: A Survey
Perera, C., Liu, C. H., & Jayawardena, S. (2015) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2015.2390034 · Cited by 555 · see citing works
“Edge mining in IoT paradigm is discussed in [7].”
Saleem, Y., Crespi, N., Rehmani, M. H., & Copeland, R. (2019) · IEEE Access · doi:10.1109/access.2019.2913984 · Cited by 534 · see citing works
Wahab, O. A., Mourad, A., Otrok, H., & Taleb, T. (2021) · IEEE Communications Surveys and Tutorials · doi:10.1109/comst.2021.3058573 · Cited by 444 · see citing works
“Bias recently began to attract a great attention in modern machine learning systems [84], [85].”
BackgroundFog Assisted-IoT Enabled Patient Health Monitoring in Smart Homes
Verma, P., & Sood, S. K. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2803201 · Cited by 473 · see citing works
“To classify the patient event data as abnormal or normal, Bayesian belief network (BBN) classifier is used [16].”
MethodMachine learning based solutions for security of Internet of Things (IoT): A survey
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2020.102630 · Cited by 483 · see citing works
“Most of the devices in IoT layers are plug and play types from where a huge portion of big data are produced [32]-[36].”
BackgroundContext-Aware Computing, Learning, and Big Data in Internet of Things: A Survey
Sezer, Ö. B., Doğdu, E., & Özbayoğlu, A. M. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2773600 · Cited by 453 · see citing works
“5) Survey of data mining [27], [31], [32].”
BackgroundA gap analysis of Internet-of-Things platforms
Mineraud, J., Mazhelis, O., Su, X. J., & Tarkoma, S. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.03.015 · Cited by 442 · see citing works
Ti3C2Tx MXene-Based Flexible Piezoresistive Physical Sensors
Wang, Y., Yang, Y., Cheng, F., Cheng, Y., Ge, B., Liu, N., & Gao, Y. (2022) · ACS Nano · doi:10.1021/acsnano.1c09925 · Cited by 415 · see citing works
Internet of Things-enabled smart cities: State-of-the-art and future trends
Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018) · Measurement · doi:10.1016/j.measurement.2018.07.067 · Cited by 408 · see citing works
Data Fusion and IoT for Smart Ubiquitous Environments: A Survey
Alam, F., Mehmood, R., Katib, I. A., Albogami, N., & Albeshri, A. A. (2017) · IEEE Access · doi:10.1109/access.2017.2697839 · Cited by 389 · see citing works
“Various techniques of connecting intelligent things and acquiring key insights from sensed data from IoT are discussed in [66]–[69].”
Data Quality in Internet of Things: A state-of-the-art survey
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.08.002 · Cited by 378 · see citing works
Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond
Arshad, R., Zahoor, S., Shah, M. A., Wahid, A., & Yu, H. (2017) · IEEE Access · doi:10.1109/access.2017.2686092 · Cited by 372 · see citing works
“Therefore, imagine billions of such devices consuming energy on daily basis andmillions of GBs of data transmitted by the sensors needs to best or edited by huge Data Centers thus huge processing and analytics capabilities are needed [15], [16], which consume a lot of energy resources, and to further deepen the crisis…”
BackgroundAuthentication Protocols for Internet of Things: A Comprehensive Survey
Ferrag, M. A., Maglaras, L. A., Janicke, H., Jiang, J., & Shu, L. (2017) · Security and Communication Networks · doi:10.1155/2017/6562953 · Cited by 331 · see citing works
A Survey of Utility-Oriented Pattern Mining
Gan, W., Lin, J. C.-W., Fournier-Viger, P., Chao, H.-C., Tseng, V. S., & Yu, P. S. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2942594 · Cited by 320 · see citing works
“Mining interesting patterns from different types of data is quite important in many real-life applications [1], [3], [4], [5], [6].”
BackgroundDesign architectures for energy harvesting in the Internet of Things
Zeadally, S., Shaikh, F. K., Talpur, A., & Sheng, Q. Z. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109901 · Cited by 328 · see citing works
“Recently, we have witnessed a significant increase in the amount of data produced by IoT [10,11] despite the use of scarce energy resources [12].”
An overview of security and privacy in smart cities' IoT communications
Al-Turjman, F. M., Zahmatkesh, H., & Shahroze, R. (2019) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3677 · Cited by 327 · see citing works
“Tsai et al 88 showed that the huge amount of data collected by various sensors in smart cities are utilized to mine new information and regulations which in turn provides better services to the users.”
BackgroundSecurity and Privacy in Smart Cities: Challenges and Opportunities
Cui, L., Xie, G., Qu, Y., Gao, L., & Yang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2853985 · Cited by 316 · see citing works
“In the field of data mining (DM), a comprehensive survey conducted by Tsai et al. [63] indicated that vast quantities of data collected by many sensors and devices around consumers are used to mine new regulations and information to provide better services.”
BackgroundChoices for interaction with things on Internet and underlying issues
Mashal, I., Alsaryrah, O., Chung, T.-Y. D., Yang, C., Kuo, W., & Agrawal, D. P. (2015) · Ad Hoc Networks · doi:10.1016/j.adhoc.2014.12.006 · Cited by 304 · see citing works
“[24] analyze and discuss several studies on applying data mining techniques for the IoT from different perspective.”
BackgroundSecuring the Internet of Things in the Age of Machine Learning and Software-Defined Networking
Restuccia, F., D'Oro, S., & Melodia, T. (2018) · IEEE Internet of Things Journal · doi:10.1109/jiot.2018.2846040 · Cited by 286 · see citing works
“by the IoT network, data mining and ML approaches can be proficiently leveraged to provide improved services to citizens such as traffic management and e-health monitoring [119].”
BackgroundAhmadi, H., Arji, G., Shahmoradi, L., Safdari, R., Nilashi, M., & Alizadeh, M. (2018) · Universal Access in the Information Society · doi:10.1007/s10209-018-0618-4 · Cited by 281 · see citing works
“On the other hand, in an IoT environment, because of some intrinsic features such as high volume, high veracity and high volatility, traditional machine learning algorithms cannot be applied to process the resulting large amounts of data [226, 227].”
BackgroundMachine learning and data analytics for the IoT
Adi, E., Anwar, A., Baig, Z., & Zeadally, S. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04874-y · Cited by 279 · see citing works
Varatharajan, R., Manogaran, G., Priyan, M. K., & Sundarasekar, R. (2017) · Cluster Computing · doi:10.1007/s10586-017-0977-2 · Cited by 277 · see citing works
“IoT is applied in many applications to get better results [8–10].”
BackgroundOsman, A. I., Nasr, M., Eltaweil, A. S., Hosny, M., Farghali, M. R. F., Al-Fatesh, A. S., Rooney, D. W., & El-Monaem, E. M. A. (2024) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2024.03.223 · Cited by 276 · see citing works
Future Internet of Things: open issues and challenges
Tsai, C., Lai, C., & Vasilakos, A. V. (2014) · Wireless Networks · doi:10.1007/s11276-014-0731-0 · Cited by 273 · see citing works
“For the details of other data mining technologies for IoT, you are referred to [83].”
BackgroundSelf-citationThe evolution of the Internet of Things (IoT) over the past 20 years
Wang, J., Lim, M. K., Wang, C., & Tseng, M. (2021) · Computers & Industrial Engineering · doi:10.1016/j.cie.2021.107174 · Cited by 290 · see citing works
A Survey of Parallel Sequential Pattern Mining
Gan, W., Lin, J. C.-W., Fournier‐Viger, P., Chao, H.-C., & Yu, P. S. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3314107 · Cited by 270 · see citing works
“of them? The methods summarized in this article not only fit for parallel computing [7, 16, 111], but are also good references for other related work, such as data mining [21], big data technologies [66, 68, 117, 118, 125], distributed systems [17, 113], and database management [72]. Thus, this paper aims at reviewing…”
BackgroundInternet of Ships: A Survey on Architectures, Emerging Applications, and Challenges
Aslam, S., Michaelides, M. P., & Herodotou, H. (2020) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.2993411 · Cited by 261 · see citing works
“For instance, Tsai et al. [3] presented a survey on data mining solutions for IoT, while Botta et al. [4] focused on the integration of cloud computing with IoT in order to benefit from the smart city concept.”
BackgroundThe Internet of Things: A Review of Enabled Technologies and Future Challenges
Din, I. U., Guizani, M., Hassan, S., Kim, B.-S., Khan, M. K., Atiquzzaman, M., & Ahmed, S. H. (2018) · IEEE Access · doi:10.1109/access.2018.2886601 · Cited by 262 · see citing works
“This section is based on the following IoT-related papers: i) smart cities [25] that consist of traffic management, virtual power plants, emergency and health systems; ii) heterogeneous IoT [5] that elaborates the functions of application, cloud, networking, and sensing layers; iii) fog computing [19], which elucidate…”
BackgroundInfluentialAg-IoT for crop and environment monitoring: Past, present, and future
Chamara, N., Islam, M. D., Bai, G., Shi, Y., & Ge, Y. (2022) · Agricultural Systems · doi:10.1016/j.agsy.2022.103497 · Cited by 254 · see citing works
Bostani, H., & Sheikhan, M. (2016) · Computer Communications · doi:10.1016/j.comcom.2016.12.001 · Cited by 253 · see citing works
Leveraging Machine Learning and Big Data for Smart Buildings: A Comprehensive Survey
Qolomany, B., Al‐Fuqaha, A., Gupta, A. K., Benhaddou, D., Alwajidi, S., Qadir, J., & Fong, A. C. M. (2019) · IEEE Access · doi:10.1109/access.2019.2926642 · Cited by 256 · see citing works
“are assigned to the right category, which is also known as the accuracy rate (AR) defined by [21].”
BackgroundMethodInfluentialBig Data Meet Cyber-Physical Systems: A Panoramic Survey
Atat, R., Liu, L., Wu, J., Li, G., Ye, C., & Yang, Y. (2018) · IEEE Access · doi:10.1109/access.2018.2878681 · Cited by 247 · see citing works
Yu, Y., Tang, J., Huang, J., Zhang, X. Y., So, D. K. C., & Wong, K. (2021) · IEEE Transactions on Communications · doi:10.1109/tcomm.2021.3089476 · Cited by 247 · see citing works
“, power grid monitoring [4] and temperature and humidity monitoring [5].”
BackgroundA Review on Internet of Things for Defense and Public Safety
Fraga-Lamas, P., Fernández-Caramés, T., Suárez-Albela, M., Castedo, L., & González-López, M. (2016) · Sensors · doi:10.3390/s16101644 · Cited by 242 · see citing works
“There are some platforms for big data analytics like Apache Hadoop and SciDB [133].”
MethodIOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector
Muthu, B., Sivaparthipan, C. B., Manogaran, G., Sundarasekar, R., Kadry, S., Shanthini, A., & Dasel, A. A. (2020) · Peer-to-Peer Networking and Applications · doi:10.1007/s12083-019-00823-2 · Cited by 243 · see citing works
Bishara, D., Xie, Y., Liu, W. K., & Li, S. (2022) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-022-09795-8 · Cited by 228 · see citing works
“In recent years, ML rose to prominence in computational mechanics although ML was first studied in 1959 [8], and has been widely applied in computer vision, general game playing, economics, and data mining, among other areas [9–12].”
BackgroundInternet of Things: an overview
Khodadadi, F., Dastjerdi, A. V., & Buyya, R. (2016) · Elsevier eBooks · doi:10.1016/b978-0-12-805395-9.00001-0 · Cited by 233 · see citing works
Edge Mesh: A New Paradigm to Enable Distributed Intelligence in Internet of Things
Sahni, Y., Cao, J., Zhang, S., & Yang, L. (2017) · IEEE Access · doi:10.1109/access.2017.2739804 · Cited by 221 · see citing works
“clearly defined what is intelligence, in the context of IoT? and who provides it? These types of questions have motivated researchers to study about topics such as data-centric IoT [1], data mining in IoT [2], the interaction of artificial intelligence with IoT etc.”
BackgroundSelf-citationTrust Management Techniques for the Internet of Things: A Survey
Din, I. U., Guizani, M., Kim, B.-S., Hassan, S., & Khan, M. K. (2018) · IEEE Access · doi:10.1109/access.2018.2880838 · Cited by 212 · see citing works
“The IoT development brings a remarkable effect to various areas, such as smart cities [3], smart healthcare [4], smart transportation [5], cellular communications [6], data mining [7], manufacturing [8], and environmental monitoring [9] among others [10]–[13].”
BackgroundInternet of Things: A Review of Surveys Based on Context Aware Intelligent Services
Gil, D., Ferrández, A., Mora, H., & Peral, J. (2016) · Sensors · doi:10.3390/s16071069 · Cited by 206 · see citing works
“The survey presented in [32] summarizes the features of data mining for IoT.”
BackgroundYounan, M., Houssein, E. H., Elhoseny, M., & Ali, A. A. (2019) · Measurement · doi:10.1016/j.measurement.2019.107198 · Cited by 207 · see citing works
Industrie 4.0: Enabling technologies
Wan, J., Cai, H., & Zhou, K. (2015) · International Conference on Intelligent Computing and Internet of Things · doi:10.1109/icaiot.2015.7111555 · Cited by 202 · see citing works
A Survey on Methods and Theories of Quantized Neural NetworksPreprint
Guo, Y. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04752 · Cited by 249 · see citing works
A Survey of Distributed Data Aggregation Algorithms
Jesus, P., Baquero, C., & Almeida, P. S. (2014) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2014.2354398 · Cited by 193 · see citing works
Survey of platforms for massive IoT
Hejazi, H., Rajab, H., Cinkler, T., & Lengyel, L. (2018) · doi:10.1109/fiot.2018.8325598 · Cited by 184 · see citing works
“A general survey on M2M communication protocols can describe in [19].”
BackgroundGumusbas, D., Yıldırım, T., Genovese, A., & Scotti, F. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.2992966 · Cited by 183 · see citing works
“In a similar manner, the review presented in [25] discusses DM concepts with IoT applications.”
BackgroundApplying IoT Sensors and Big Data to Improve Precision Crop Production: A Review
Alahmad, T., Neményi, M., & Nyéki, A. (2023) · Agronomy · doi:10.3390/agronomy13102603 · Cited by 172 · see citing works
“These data are mined and processed in order to show the relationships between inputs and outputs [31].”
MethodAtat, R., Liu, L., Chen, H., Wu, J., Li, H., & Yi, Y. (2017) · IET Cyber-Physical Systems Theory & Applications · doi:10.1049/iet-cps.2017.0010 · Cited by 162 · see citing works
Technical Issues on Cognitive Radio-Based Internet of Things Systems: A Survey
Awin, F., Alginahi, Y. M., Abdel‐Raheem, E., & Tepe, K. E. (2019) · IEEE Access · doi:10.1109/access.2019.2929915 · Cited by 158 · see citing works
Shi, Z., Yang, W., Deng, X., Cai, C., Yan, Y., Liang, H., Liu, Z., & Qiao, Z. (2020) · Molecular Systems Design & Engineering · doi:10.1039/d0me00005a · Cited by 157 · see citing works
Cybermatics: Cyber–physical–social–thinking hyperspace based science and technology
Ning, H., Liu, H., Ma, J., Yang, L. T., & Huang, R. (2015) · Future Generation Computer Systems · doi:10.1016/j.future.2015.07.012 · Cited by 156 · see citing works
“Generally, data mining can be classified into four categories: clustering (classify patterns-unlabeled), classification (classify patterns-labeled), association rules (find events-nonsequential), and sequential patterns (find events-sequential) [49].”
BackgroundSelf-citationMeltzer, J. P. (2019) · World Trade Review · doi:10.1017/s1474745618000502 · Cited by 145 · see citing works
“54 Chun-Wei Tsai et al. (2014), ‘DataMining for Internet of Things: A Survey’, IEEE Communications Surveys & Tutorials, 16(1): 77–97.”
BackgroundGreen IoT: A Review and Future Research Directions
Alsharif, M. H., Jahid, A., Kelechi, A. H., & Kannadasan, R. (2023) · Symmetry · doi:10.3390/sym15030757 · Cited by 144 · see citing works
“…traffic will be machine-to-machine (M2M) traffic by 2024, thanks to the IoT ’ s ability to allow physical objects to communicate among themselves and perform tasks with zero human intervention. have significant processing and analytics capabilities [7,8], which can be energy-intensive considered.”
BackgroundRole of machine learning and deep learning in securing 5G-driven industrial IoT applications
Sharma, P., Jain, S., Gupta, S. R., & Chamola, V. (2021) · Ad Hoc Networks · doi:10.1016/j.adhoc.2021.102685 · Cited by 146 · see citing works
Ramírez-Gallego, S., Fernández, A., García, S., Chen, M., & Herrera, F. (2018) · Information Fusion · doi:10.1016/j.inffus.2017.10.001 · Cited by 140 · see citing works
Amma, N. G. B., & Selvakumar, S. (2020) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.020 · Cited by 141 · see citing works
Predictive Analytics for Complex IoT Data Streams
Akbar, A., Khan, A., Carrez, F., & Moessner, K. (2017) · IEEE Internet of Things Journal · doi:10.1109/jiot.2017.2712672 · Cited by 136 · see citing works
“Whereas ML methods are targeted for applications which are based on the historical data for extraction of knowledge [10].”
MethodSchmidt, M., & Åhlund, C. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.013 · Cited by 134 · see citing works
“documented in [15, 54, 55] often helps to achieve satisfactory performance of the subsequent applications.”
BackgroundThe Internet of Things (IoT): a survey of techniques, operating systems, and trends
Shammar, E. A., & Zahary, A. T. (2019) · Library Hi Tech · doi:10.1108/lht-12-2018-0200 · Cited by 146 · see citing works
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications
Messaoud, S., Bradai, A., Bukhari, S. H. R., Quang, P. T. A., Ahmed, O. B., & Atri, M. (2020) · Internet of Things · doi:10.1016/j.iot.2020.100314 · Cited by 130 · see citing works
Software defined healthcare networks
Hu, L., Meikang Qiu, A., Song, J., Hossain, M. S., & Ghoneim, A. (2015) · IEEE Wireless Communications · doi:10.1109/mwc.2015.7368826 · Cited by 125 · see citing works
“Since these strategies further increase the complexity of traffic scheduling, some approximation algorithms shall be developed to solve the problem of traffic scheduling efficiently [12].”
BackgroundA Systematic Review for Smart City Data Analytics
Moustaka, V., Vakali, A. I., & Anthopoulos, L. G. (2018) · ACM Computing Surveys · doi:10.1145/3239566 · Cited by 122 · see citing works
Liu, R. W., Guo, Y., Nie, J., Hu, Q., Xiong, Z., Yu, H., & Guizani, M. (2022) · IEEE Transactions on Green Communications and Networking · doi:10.1109/tgcn.2022.3158004 · Cited by 122 · see citing works
“The previous data mining techniques, developed for traditional IoT systems [27], [28], could be theoretically extended to MIoT.”
MethodDeep Learning in Mobile and Wireless Networking: A SurveyPreprint
Zhang, C., Patras, P., & Haddadi, H. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.04311 · Cited by 121 · see citing works
“outlook of deep learning for recommender systems. D 7 7 Yu et al. [33] A survey on networking big data. D Alsheikh et al. [34] A survey on machine learning in wireless sensor networks. D Tsai et al. [35] A survey on data mining in IoT. D Cheng et al. [36] An introductions mobile big data its applications. D 7 Bkassiny…”
BackgroundHe, H., Maple, C. R., Watson, T., Tiwari, A., Mehnen, J., Jin, Y., & Gabryś, B. (2016) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec.2016.7743900 · Cited by 120 · see citing works
“[41] generalised an architecture of IoT with KDD (Fig.”
BackgroundEnergy Harvesting Techniques for Wireless Sensor Networks/Radio-Frequency Identification: A Review
Alsharif, M. H., Kim, S., & Kuruoğlu, N. (2019) · Symmetry · doi:10.3390/sym11070865 · Cited by 120 · see citing works
“To process these enormous sensor payload, huge processing and analytics capabilities are needed, which, in turn, consumes huge energy resources [7,8].”
BackgroundSystematic survey of big data and data mining in internet of things
Shadroo, S., & Rahmani, A. M. (2018) · Computer Networks · doi:10.1016/j.comnet.2018.04.001 · Cited by 117 · see citing works
“control, and monitor each object on earth (usually called things) via the internet [1, 2, 3, 4, 5].”
BackgroundInfluentialA Distributed Consensus Algorithm for Decision Making in Service-Oriented Internet of Things
Li, S., Oikonomou, G., Tryfonas, T., Chen, T., & Xu, L. D. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2306331 · Cited by 116 · see citing works
“1, a three-layer architecture of the IoT is summarized [1] – [6], [9], [11], [12], which contains three basic layers: 1) the application layer ; 2) the network layer ; and 3) the sensing layer .”
BackgroundEvaluation of design alternatives of End-Of-Life products using internet of things
Joshi, A. D., & Gupta, S. M. (2018) · International Journal of Production Economics · doi:10.1016/j.ijpe.2018.12.010 · Cited by 115 · see citing works
Qays, M. O., Ahmad, I., Abu‐Siada, A., Hossain, M. L., & Yasmin, F. (2023) · Energy Reports · doi:10.1016/j.egyr.2023.01.085 · Cited by 114 · see citing works
Internet of things and data mining: An application oriented survey
Sunhare, P., Chowdhary, R. R., & Chattopadhyay, M. K. (2020) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2020.07.002 · Cited by 113 · see citing works
A multi-stage anomaly detection scheme for augmenting the security in IoT-enabled applications
Garg, S., Kaur, K., Batra, S., Kaddoum, G., Kumar, N., & Boukerche, A. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.09.038 · Cited by 112 · see citing works
A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing
Savaglio, C., & Fortino, G. (2021) · ACM Transactions on Internet Technology · doi:10.1145/3402444 · Cited by 110 · see citing works
Ullah, I., Adhikari, D., Su, X., Palmieri, F., Wu, C., & Choi, C. (2024) · Digital Communications and Networks · doi:10.1016/j.dcan.2024.02.007 · Cited by 111 · see citing works
Analyzing challenges to Internet of Things (IoT) adoption and diffusion: An Indian context
Luthra, S., Garg, D., Mangla, S. K., & Berwal, Y. P. S. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2017.12.094 · Cited by 108 · see citing works
Bansal, M., Chana, I., & Clarke, S. (2020) · ACM Computing Surveys · doi:10.1145/3419634 · Cited by 110 · see citing works
“Specifically, we categorize the surveys related to IoTBD into three main categories: (1) surveys on IoT big data analysis [1, 91, 94, 98, 130, 154]; (2) domain-specific surveys on IoTBD [53, 54, 59, 83, 103]; (3) surveys on challenges in IoTBD [23, 37, 43, 76, 78, 134].”
MethodIntelligent integrated sensing and communication: a survey
Zhang, J., Lu, W., Xing, C., Zhao, N., Al‐Dhahir, N., Karagiannidis, G. K., & Yang, X. (2024) · Science China Information Sciences · doi:10.1007/s11432-024-4205-8 · Cited by 107 · see citing works
“IoT aims to connect all things, such as various devices, vehicles, and sensors, via the Internet and facilitate the efficient exchange of information among them [147].”
Towards Efficient Mobile M2M Communications: Survey and Open Challenges
Pereira, C., & Aguiar, A. (2014) · Sensors · doi:10.3390/s141019582 · Cited by 104 · see citing works
“Other M2M communications’ surveys focus mainly in home networks [16], 3GPP Long-Term Evolution (LTE) networks [17], service platforms [18], or data mining techniques [19].”
BackgroundInformation traceability platforms for asset data lifecycle: blockchain-based technologies
Brandín, R., & Abrishami, S. (2021) · Smart and Sustainable Built Environment · doi:10.1108/sasbe-03-2021-0042 · Cited by 113 · see citing works
Montori, F., Bedogni, L., Felice, M. D., & Bononi, L. (2018) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2018.08.002 · Cited by 103 · see citing works
“issue-specific surveys), large attention is devoted to sensor data management and knowledge extraction [24][25][26][27][28][29][30].”
BackgroundDeep learning based underwater acoustic OFDM communications
Zhang, Y., Li, J., Zakharov, Y. V., Li, X., & Li, J. (2019) · Applied Acoustics · doi:10.1016/j.apacoust.2019.04.023 · Cited by 99 · see citing works
“tial research works demonstrate the successful application of deep learning in various communication applications[13, 14].”
BackgroundReviewing Cyber-Physical System as a Part of Smart Factory in Industry 4.0
Sinha, D., & Roy, R. (2020) · IEEE Engineering Management Review · doi:10.1109/emr.2020.2992606 · Cited by 98 · see citing works
“Emerging IT including IoT, IoS, networked control wireless sensor-actuator network (WSAN), data mining [15], [16], big data [17] handling, cloud [18], fog, edge computing, smartphones, smart devices, high-performance computers, 5G/6G communication, high-speed Internet, arti fi cial intelligence (AI)…”
BackgroundTracing Knowledge Development Trajectories of the Internet of Things Domain: A Main Path Analysis
Fu, H., Wang, M., Li, P., Jiang, S., Hu, W., Guo, X., & Cao, M. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2929414 · Cited by 94 · see citing works
“In 2014, Tsai analyzed types of IoT data and the characteristics of data mining, and in another article in the same year, proposed a data mining and intelligent data management framework based on group optimization [20], [54].”
An overview of smart agriculture using internet of things (IoT) and web services
Choudhary, V., Guha, P., Pau, G., & Mishra, S. (2025) · Environmental and Sustainability Indicators · doi:10.1016/j.indic.2025.100607 · Cited by 94 · see citing works
Optimizing Information Freshness in Computing-Enabled IoT Networks
Xu, C., Yang, H. H., Wang, X., & Quek, T. Q. S. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2947419 · Cited by 93 · see citing works
“With the help of IoT, devices can sense and even interact with the physical surrounding environment, thereby providing us with many valuable and remarkable context-aware real time applications at an efficient cost, such as automatic control of electric appliance [5], intelligent transportation network [6], and event m…”
Background
- 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.
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