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Cited by 921Internet of Things and AIIoT and Edge/Fog ComputingIoT Networks and Protocols

Machine learning for internet of things data analysis: a survey

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Mahdavinejad, Mohammad Saeid, Rezvan, Mohammadreza, Barekatain, Mohammadamin, Adibi, Peyman, Barnaghi, Payam, Sheth, Amit P.·2018·Digital Communications and Networks
DOI 10.1016/j.dcan.2017.10.002
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Abstract

Rapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.

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  1. [2][2] C. Cecchinel, M. Jimenez, S. Mosser, M. Riveill, An architecture to support the collection of big data in the internet of things, in: 2014 IEEE World Congress on Services, IEEE, 2014, pp. 442-449. -
  2. [3][3] M. Weiser, The computer for the 21st century., Mobile Computing and Communications Review 3 (3) (1999) 3-11. -
  3. [6][6] A. Sheth, Transforming big data into smart data: Deriving value via harnessing volume, variety, and velocity using semantic techniques and technologies, in: Data Engineering (ICDE), 2014 IEEE 30th International Conference on, IEEE, 2014, pp. 2-2. -
  4. [9][9] S. Bin, L. Yuan, W. Xiaoyi, Research on data mining models for the internet of things, in: 2010 International Conference on Image Analysis and Signal Processing, IEEE, 2010, pp. 127-132. -
  5. [10][10] H. Gonzalez, J. Han, X. Li, D. Klabjan, Warehousing and analyzing massive rfid data sets, in: 22nd International Conference on Data Engineering (ICDE'06), IEEE, 2006, pp. 83-83. -
  6. [11][11] F. Chen, P. Deng, J. Wan, D. Zhang, A. V. Vasilakos, X. Rong, Data mining for the internet of things: literature review and challenges, International Journal of Distributed Sensor Networks 2015 (2015) 12. -
  7. [15][15] M. Ma, P. Wang, C.-H. Chu, Ltcep: Efficient long-term event processing for internet of things data streams, in: 2015 IEEE International Conference on Data Science and Data Intensive Systems, IEEE, 2015, pp. 548-555. -
  8. [16][16] P. Barnaghi, A. Sheth, The internet of things: The story so far, IEEE Internet of Things. -
  9. [18][18] F. Bonomi, R. Milito, J. Zhu, S. Addepalli, Fog computing and its role in the internet of things, in: Proceedings of the first edition of the MCC workshop on Mobile cloud computing, ACM, 2012, pp. 13-16. -
  10. [20][20] Y. Shi, G. Ding, H. Wang, H. E. Roman, S. Lu, The fog computing service for healthcare, in: Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech), 2015 2nd International Symposium on, IEEE, 2015, pp. 1-5. -
  11. [21][21] F. Ramalho, A. Neto, K. Santos, N. Agoulmine, et al., Enhancing ehealth smart applications: A fog-enabled approach, in: 2015 17th International Conference on E-health Networking, Application & Services (HealthCom), IEEE, 2015, pp. 323-328. -
  12. [23][23] A. Papageorgiou, M. Zahn, E. Kovacs, Efficient auto-configuration of energy-related parameters in cloud-based iot platforms, in: Cloud Networking (CloudNet), 2014 IEEE 3rd International Conference on, IEEE, 2014, pp. 236-241. -
  13. [25][25] H. Zhao, C. Huang, A data processing algorithm in epc internet of things, in: Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2014 International Conference on, IEEE, 2014, pp. 128-131. -
  14. [29][29] B. Bowerman, J. Braverman, J. Taylor, H. Todosow, U. Von Wimmersperg, The vision of a smart city, in: 2nd International Life Extension Technology Workshop, Paris, Vol. 28, 2000. -
  15. [32][32] Y. Wang, J. Yuan, X. Chen, J. Bao, Smart grid time series big data processing system, in: 2015 IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), IEEE, 2015, pp. 393-400. -
  16. [33][33] S. Karnouskos, T. N. De Holanda, Simulation of a smart grid city with software agents, in: Computer Modeling and Simulation, 2009. EMS'09. Third UKSim European Symposium on, IEEE, 2009, pp. 424-429. -
  17. [34][34] D. R. Nagesh, J. V. Krishna, S. Tulasiram, A real-time architecture for smart energy management, in: Innovative Smart Grid Technologies (ISGT), 2010, IEEE, 2010, pp. 1-4. -
  18. [35][35] T. Robles, R. Alcarria, D. Mart´ ın, A. Morales, M. Navarro, R. Calero, S. Iglesias, M. L´ opez, An internet of things-based model for smart water management, in: Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on, IEEE, 2014, pp. 821-826. -
  19. [37][37] M. M. Rathore, A. Ahmad, A. Paul, G. Jeon, Efficient graph-oriented smart transportation using internet of things generated big data, in: 2015 11th International Conference on Signal-Image Technology & InternetBased Systems (SITIS), IEEE, 2015, pp. 512-519. -
  20. [38][38] C. Costa, M. Y. Santos, Improving cities sustainability through the use of data mining in a context of big city data, in: The 2015 International Conference of Data Mining and Knowledge Engineering, Vol. 1, IAENG, 2015, pp. 320-325. -
  21. [39][39] A. J. Jara, D. Genoud, Y. Bocchi, Big data in smart cities: from poisson to human dynamics, in: Advanced Information Networking and Applications Workshops (WAINA), 2014 28th International Conference on, IEEE, 2014, pp. 785-790. -
  22. [40][40] H. Wang, O. L. Osen, G. Li, W. Li, H.-N. Dai, W. Zeng, Big data and industrial internet of things for the maritime industry in northwestern norway, in: TENCON 2015-2015 IEEE Region 10 Conference, IEEE, 2015, pp. 1-5. -
  23. [41][41] P. Barnaghi, M. Bermudez-Edo, R. T¨ onjes, Challenges for quality of data in smart cities, Journal of Data and Information Quality (JDIQ) 6 (2-3) (2015) 6. -
  24. [45][45] V. Jakkula, D. Cook, Outlier detection in smart environment structured power datasets, in: Sixth International Conference on Intelligent Environments (IE), 2010, IEEE, 2010, pp. 29-33. -
  25. [46][46] P. Ni, C. Zhang, Y. Ji, A hybrid method for short-term sensor data forecasting in internet of things, in: 2014 11th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2014. -
  26. [48][48] W. Derguech, E. Bruke, E. Curry, An autonomic approach to real-time predictive analytics using open data and internet of things, in: Ubiquitous Intelligence and Computing, 2014 IEEE 11th Intl Conf on and IEEE 11th Intl Conf on and Autonomic and Trusted Computing, and IEEE 14th Intl Conf on Scalable Computing and Communications and Its Associated Workshops (UTC-ATC-ScalCom), IEEE, 2014, pp. 204-211. -
  27. [49][49] W. Han, Y. Gu, Y. Zhang, L. Zheng, Data driven quantitative trust model for the internet of agricultural things, in: Internet of Things (IOT), 2014 International Conference on the, IEEE, 2014, pp. 31-36. -
  28. [52][52] M. Shukla, Y. Kosta, P. Chauhan, Analysis and evaluation of outlier detection algorithms in data streams, in: International Conference on Computer, Communication and Control (IC4), 2015, IEEE, 2015, pp. 1-8. -
  29. [53][53] A. Shilton, S. Rajasegarar, C. Leckie, M. Palaniswami, Dp1svm: A dynamic planar one-class support vector machine for internet of things environment, in: International Conference on Recent Advances in Internet of Things (RIoT), 2015, IEEE, 2015, pp. 1-6. -
  30. [54][54] D. Barber, Bayesian reasoning and machine learning, Cambridge University Press, 2012. -
  31. [55][55] C. M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. -
  32. [56][56] K. P. Murphy, Machine learning: a probabilistic perspective, MIT press, 2012. -
  33. [57][57] I. G. Y. Bengio, A. Courville, Deep learning, book in preparation for MIT Press (2016). -
  34. [59][59] H. V. Jagadish, B. C. Ooi, K.-L. Tan, C. Yu, R. Zhang, idistance: An adaptive b+-tree based indexing method for nearest neighbor search, ACM Transactions on Database Systems (TODS) 30 (2) (2005) 364-397. -
  35. [60][60] A. McCallum, K. Nigam, et al., A comparison of event models for naive bayes text classification, in: AAAI-98 workshop on learning for text categorization, Vol. 752, Citeseer, 1998, pp. 41-48. -
  36. [63][63] I. Guyon, B. Boser, V. Vapnik, Automatic capacity tuning of very large vcdimension classifiers, Advances in neural information processing systems (1993) 147-147. -
  37. [64][64] N. Cristianini, J. Shawe-Taylor, An introduction to support vector machines and other kernel-based learning methods, Cambridge university press, 2000. -
  38. [65][65] B. Scholkopf, A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, MIT press, 2001. -
  39. [66][66] J. Neter, M. H. Kutner, C. J. Nachtsheim, W. Wasserman, Applied linear statistical models, Vol. 4, Irwin Chicago, 1996. -
  40. [67][67] G. A. Seber, A. J. Lee, Linear regression analysis, Vol. 936, John Wiley & Sons, 2012. -
  41. [68][68] D. C. Montgomery, E. A. Peck, G. G. Vining, Introduction to linear regression analysis, John Wiley & Sons, 2015. -
  42. [71][71] L. Breiman, J. Friedman, C. J. Stone, R. A. Olshen, Classification and regression trees, CRC press, 1984. -
  43. [78][78] V. Jumutc, R. Langone, J. A. Suykens, Regularized and sparse stochastic k-means for distributed large-scale clustering, in: Big Data (Big Data), 2015 IEEE International Conference on, IEEE, 2015, pp. 2535-2540. -
  44. [79][79] M. Ester, H.-P. Kriegel, J. Sander, X. Xu, et al., A density-based algorithm for discovering clusters in large spatial databases with noise., in: Kdd, Vol. 96, 1996, pp. 226-231. -
  45. [84][84] I. Jolliffe, Principal component analysis, Wiley Online Library, 2002. -
  46. [88][88] F. R. Bach, M. I. Jordan, Kernel independent component analysis, Journal of machine learning research 3 (Jul) (2002) 1-48. -
  47. [90][90] X. Glorot, Y. Bengio, Understanding the difficulty of training deep feedforward neural networks., in: Aistats, Vol. 9, 2010, pp. 249-256. -
  48. [91][91] R. C. Eberhart, Neural network PC tools: a practical guide, Academic Press, 2014. -
  49. [96][96] C.-T. Do, A. Douzal-Chouakria, S. Mari´ e, M. Rombaut, Multiple metric learning for large margin knn classification of time series, in: Signal Processing Conference (EUSIPCO), 2015 23rd European, IEEE, 2015, pp. 2346-2350. -
  50. [97][97] V. Metsis, I. Androutsopoulos, G. Paliouras, Spam filtering with naive bayes-which naive bayes?, in: CEAS, 2006, pp. 27-28. -
  51. [102][102] C.-N. J. Yu, T. Joachims, Learning structural svms with latent variables, in: Proceedings of the 26th annual international conference on machine learning, ACM, 2009, pp. 1169-1176. -
  52. [103][103] J. Weston, C. Watkins, et al., Support vector machines for multi-class pattern recognition., in: ESANN, Vol. 99, 1999, pp. 219-224. -
  53. [107][107] C. S. Leslie, E. Eskin, W. S. Noble, The spectrum kernel: A string kernel for svm protein classification., in: Pacific symposium on biocomputing, Vol. 7, 2002, pp. 566-575. -
  54. [109][109] M. A. Khan, A. Khan, M. N. Khan, S. Anwar, A novel learning method to classify data streams in the internet of things, in: Software Engineering Conference (NSEC), 2014 National, IEEE, 2014, pp. 61-66. -
  55. [110][110] H. Trevor, T. Robert, F. Jerome, The elements of statistical learning: data mining, inference and prediction, New York: Springer-Verlag 1 (8) (2001) 371-406. -
  56. [111][111] R. Caruana, A. Niculescu-Mizil, An empirical comparison of supervised learning algorithms, in: Proceedings of the 23rd international conference on Machine learning, ACM, 2006, pp. 161-168. -
  57. [113][113] T. Au, M.-L. I. Chin, G. Ma, Mining rare events data by sampling and boosting: A case study, in: International Conference on Information Systems, Technology and Management, Springer, 2010, pp. 373-379. -
  58. [114][114] A. Sahu, G. Runger, D. Apley, Image denoising with a multi-phase kernel principal component approach and an ensemble version, in: 2011 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), IEEE, 2011, pp. 1-7. -
  59. [116][116] X. Tao, C. Ji, Clustering massive small data for iot, in: 2nd International Conference on Systems and Informatics (ICSAI), 2014, IEEE, 2014, pp. 974-978. -
  60. [117][117] H. Hromic, D. Le Phuoc, M. Serrano, A. Antoni´ c, I. P. ˇ Zarko, C. Hayes, S. Decker, Real time analysis of sensor data for the internet of things by means of clustering and event processing, in: 2015 IEEE International Conference on Communications (ICC), IEEE, 2015, pp. 685-691. -
  61. [119][119] G. H. Golub, C. F. Van Loan, Matrix computations, Vol. 3, JHU Press, 2012. -
  62. [126][126] I. Kotenko, I. Saenko, F. Skorik, S. Bushuev, Neural network approach to forecast the state of the internet of things elements, in: XVIII International Conference on Soft Computing and Measurements (SCM), 2015, IEEE, 2015, pp. 133-135. -
  63. [127][127] T. G. Dietterich, Machine learning for sequential data: A review, in: Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Springer, 2002, pp. 15-30. -
  64. [132][132] J. Lafferty, A. McCallum, F. Pereira, Conditional random fields: Probabilistic models for segmenting and labeling sequence data, in: Proceedings of the eighteenth international conference on machine learning, ICML, Vol. 1, 2001, pp. 282-289. -
  65. [134][134] A. McCallum, D. Freitag, F. C. Pereira, Maximum entropy markov models for information extraction and segmentation., in: Icml, Vol. 17, 2000, pp. 591-598. -
  66. [135][135] H. Sak, A. W. Senior, F. Beaufays, Long short-term memory recurrent neural network architectures for large scale acoustic modeling., in: INTERSPEECH, 2014, pp. 338-342. -
  67. [137][137] B. Pardo, W. Birmingham, Modeling form for on-line following of musical performances, in: PROCEEDINGS OF THE NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE, Vol. 20, Menlo Park, CA; Cambridge, MA; London; AAAI Press; MIT Press; 1999, 2005, p. 1018. -
  68. [140][140] Z. ´ A. Milacski, M. Ludersdorfer, A. L˝ orincz, P. van der Smagt, Robust detection of anomalies via sparse methods, in: International Conference on Neural Information Processing, Springer, 2015, pp. 419-426. -
  69. [143][143] M. R. Smith, T. Martinez, Improving classification accuracy by identifying and removing instances that should be misclassified, in: Neural Networks (IJCNN), The 2011 International Joint Conference on, IEEE, 2011, pp. 2690-2697. -
  70. [145][145] K. A. Heller, K. M. Svore, A. D. Keromytis, S. J. Stolfo, One class support vector machines for detecting anomalous windows registry accesses, in: Proc. of the workshop on Data Mining for Computer Security, Vol. 9, 2003. -
  71. [146][146] M. Zhang, B. Xu, J. Gong, An anomaly detection model based on one-class svm to detect network intrusions, in: 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN), IEEE, 2015, pp. 102-107. -
  72. [147][147] Y. Zhang, N. Meratnia, P. Havinga, Adaptive and online one-class support vector machine-based outlier detection techniques for wireless sensor networks, in: Advanced Information Networking and Applications Workshops, 2009. WAINA'09. International Conference on, IEEE, 2009, pp. 990-995. -
  73. [148][148] S. Hu, Research on data fusion of the internet of things, in: Logistics, Informatics and Service Sciences (LISS), 2015 International Conference on, IEEE, 2015, pp. 1-5.

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Authors
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P.
Affiliations
  • Wright State University (US)
  • Technical University of Munich (DE)
  • University of Isfahan (IR)
  • University of Surrey (GB)
Corresponding author
Mahdavinejad, Mohammad Saeid
ORCID
Barnaghi, Payam
Venue
Digital Communications and Networks
Publisher
Elsevier BV
Published
2018-08
Volume
4
Issue
3
Pages
161-175
ISSN
2352-8648 (ISSN-L)
DOI
10.1016/j.dcan.2017.10.002
OpenAlex
W2762644836
Cited by
921
References
69
Citations by year
2026: 35 · 2025: 72 · 2024: 115 · 2023: 124 · 2022: 157 · 2021: 183 · 2020: 133 · 2019: 77 · 2018: 23 · 2017: 1 · 2013: 1
FWCI
119.14
Type
journal article
Language
en
Open access
Licence
Locations
  • Digital Communications and Networks (publishedVersion, cc-by-nc-nd)
  • arXiv (Cornell University) (submittedVersion)
  • Scholar Commons (University of South Carolina) (submittedVersion, cc-by-nc-nd)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • arXiv (Cornell University)
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 › Engineering › Electrical and Electronic Engineering › IoT Networks and Protocols
Keywords
Internet of Things, IoT data analytics, machine learning algorithms, smart cities, big data, sensor data, traffic data, support vector machine, Aarhus

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