Skip to main content
SpringerLink
Log in

A survey on application of machine learning for Internet of Things

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Internet of Things (IoT) has become an important network paradigm and there are lots of smart devices connected by IoT. IoT systems are producing massive data and thus more and more IoT applications and services are emerging. Machine learning, as an another important area, has obtained a great success in several research fields such as computer vision, computer graphics, natural language processing, speech recognition, decision-making, and intelligent control. It has also been introduced in networking research. Many researches study how to utilize machine learning to solve networking problems, including routing, traffic engineering, resource allocation, and security. Recently, there has been a rising trend of employing machine learning to improve IoT applications and provide IoT services such as traffic engineering, network management, security, Internet traffic classification, and quality of service optimization. This survey paper focuses on providing an overview of the application of machine learning in the domain of IoT. We provide a comprehensive survey highlighting the recent progresses in machine learning techniques for IoT and describe various IoT applications. The application of machine learning for IoT enables users to obtain deep analytics and develop efficient intelligent IoT applications. This paper is different from the previously published survey papers in terms of focus, scope, and breadth; specifically, we have written this paper to emphasize the application of machine learning for IoT and the coverage of most recent advances. This paper has made an attempt to cover the major applications of machine learning for IoT and the relevant techniques, including traffic profiling, IoT device identification, security, edge computing infrastructure, network management and typical IoT applications. We also make a discussion on research challenges and open issues.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. IoT Analytics. Why the internet of things is called internet of things: Definition, history, disambiguation. https://iot-analytics.com/internet-of-things-definition/

  2. Gartner Research. Gartner says 6.4 billion connected things will be in use in 2016, up 30 percent from 2015. http://www.gartner.com/newsroom/id/3165317

  3. Juniper Research. Internet of things connected devices to almost triple to over 38 billion units by 2020. http://www.juniperresearch.com/press/press-releases/iot-connected-devices-to-triple-to-38-bn-by-2020

  4. The Statistics Portal. Internet of things (iot): number of connected devices worldwide from 2012 to 2020 (in billions). http://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/

  5. Sharma SK, Wang X (2017) Live data analytics with collaborative edge and cloud processing in wireless iot networks. IEEE Access 5:4621–4635

    Article  Google Scholar 

  6. Chau DH, Kittur A, Hong JI, Faloutsos C (2011) Apolo: Making sense of large network data by combining rich user interaction and machine learning. In: Proceedings of the SIGCHI conference on human factors in computing systems, CHI '11, Vancouver, BC, pp 167–176

  7. Klaine PV, Imran MA, Onireti O, Souza RD (2017) A survey of machine learning techniques applied to self-organizing cellular networks. IEEE Commun Surv Tutor 19(4):2392–2431 (Fourthquarter)

  8. Suthaharan S (2014) Big data classification: problems and challenges in network intrusion prediction with machine learning. SIGMETRICS Perform Eval Rev 41(4):70–73

    Article  Google Scholar 

  9. Usama M, Qadir J, Raza A, Arif H, Yau KA, Elkhatib Y, Hussain A, Al-Fuqaha AI (2017) Unsupervised machine learning for networking: techniques, applications and research challenges. CoRR. arXiv:1709.06599 (Online)

  10. Ayoubi S, Limam N, Salahuddin MA, Shahriar N, Boutaba R, Estrada-Solano F, Caicedo OM (2018) Machine learning for cognitive network management. IEEE Commun Mag 56(1):158–165

    Article  Google Scholar 

  11. Fadlullah ZM, Tang F, Mao B, Kato N, Akashi O, Inoue T, Mizutani K (2017) State-of-the-art deep learning: evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Commun Surv Tutor 19(4):2432–2455 (Fourthquarter)

  12. Wang M, Cui Y, Wang X, Xiao S, Jiang J (2017) Machine learning for networking: Workflow, advances and opportunities. IEEE Netw 32(2):92–99

    Article  Google Scholar 

  13. Hammerschmidt CA, Garcia S, Verwer S, State R (2017) Reliable machine learning for networking: key issues and approaches. In: 2017 IEEE 42nd conference on local computer networks (LCN), Singapore, pp 167–170

  14. Casas P, Vanerio J, Fukuda K (2017) Gml learning, a generic machine learning model for network measurements analysis. In: 2017 13th international conference on network and service management (CNSM), Tokyo, pp 1–9

  15. Claffy KC, Braun H-W, Polyzos GC (1995) A parameterizable methodology for internet traffic flow profiling. IEEE J Sel Areas Commun 13(8):1481–1494

    Article  Google Scholar 

  16. Krishnamurthy B, Sen S, Zhang Y, Chen Y (2003) Sketch-based change detection: methods, evaluation, and applications. In: Proceedings of the 3rd ACM SIGCOMM conference on Internet measurement. ACM, New York, pp 234–247

  17. 1. Lakhina A, Crovella M, Diot C (2004) Diagnosing network-wide traffic anomalies. In: ACM SIGCOMM Computer Communication Review, vol 34, no 4. ACM, New York, pp 219–230

  18. Lakhina A, Crovella M, Diot C (2004) Characterization of network-wide anomalies in traffic flows. In: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement. ACM, New York, pp 201–206

  19. Xu K, Zhang Z-L, Bhattacharyya S (2008) Internet traffic behavior profiling for network security monitoring. IEEE ACM Trans Netw 16(6):1241–1252

    Article  Google Scholar 

  20. Hu Y, Chiu D-M, Lui JC (2009) Profiling and identification of p2p traffic. Comput Netw 53(6):849–863

    Article  MATH  Google Scholar 

  21. Iliofotou M, Gallagher B, Eliassi-Rad T, Xie G, Faloutsos M (2010) Profiling-by-association: a resilient traffic profiling solution for the internet backbone. In: Proceedings of the 6th International Conference. ACM, New York, p 2

  22. Iliofotou M, Kim H-C, Faloutsos M, Mitzenmacher M, Pappu P, Varghese G (2011) Graption: A graph-based p2p traffic classification framework for the internet backbone. Comput Netw 55(8):1909–1920

    Article  Google Scholar 

  23. Brauckhoff D, Dimitropoulos X, Wagner A, Salamatian K (2012) Anomaly extraction in backbone networks using association rules. IEEE ACM Trans Netw (TON) 20(6):1788–1799

    Article  Google Scholar 

  24. Huang N-F, Jai G-Y, Chao H-C, Tzang Y-J, Chang H-Y (2013) Application traffic classification at the early stage by characterizing application rounds. Inf Sci 232:130–142

    Article  Google Scholar 

  25. Glatz E, Mavromatidis S, Ager B, Dimitropoulos X (2014) Visualizing big network traffic data using frequent pattern mining and hypergraphs. Computing 96(1):27–38

    Article  Google Scholar 

  26. Bakhshi T, Ghita B (2015) User traffic profiling in a software defined networking context. In: International conference on internet technologies and applications, Wrexham, UK, September 8–11, pp 91–97. https://doi.org/10.1109/ITechA.2015.7317376

  27. Kirchler M, Herrmann D, Lindemann J, Kloft M (2016) Tracked without a trace: linking sessions of users by unsupervised learning of patterns in their dns traffic. In: Proceedings of the 2016 ACM Workshop on Artificial Intelligence and Security. ACM, New York, pp 23–34

  28. Das AK, Pathak PH, Chuah C-N, Mohapatra P (2017) Privacy-aware contextual localization using network traffic analysis. Comput Netw 118:24–36

    Article  Google Scholar 

  29. Stolfo SJ, Hershkop S, Wang K, Nimeskern O, Hu CW (2003) Behavior profiling of email. In: Chen H, Miranda R, Zeng DD, Demchak C, Schroeder J, Madhusudan T (eds) Intelligence and security informatics, ISI 2003, Lecture notes in computer science, vol 2665. Springer, Berlin, Heidelberg, pp 960–960

    Google Scholar 

  30. Stöber T, Frank M, Schmitt J, Martinovic I (2013) Who do you sync you are?: smartphone fingerprinting via application behaviour. In: Proceedings of the sixth ACM conference on security and privacy in wireless and mobile networks. ACM, Budapest, Hungary, pp 7–12

  31. Das A, Borisov N, Caesar M (2014) Do you hear what i hear?: fingerprinting smart devices through embedded acoustic components. In: Proceedings of the 2014 ACM SIGSAC conference on computer and communications security. ACM, Scottsdale, Arizona, pp 441–452

  32. Bojinov H, Michalevsky Y, Nakibly G, Boneh D (2014) Mobile device identification via sensor fingerprinting. arXiv:1408.1416

  33. Patel HJ, Temple MA, Baldwin RO (2015) Improving zigbee device network authentication using ensemble decision tree classifiers with radio frequency distinct native attribute fingerprinting. IEEE Trans Reliab 64(1):221–233

    Article  Google Scholar 

  34. Huynh M, Nguyen P, Gruteser M, Vu T (2015) Poster: Mobile device identification by leveraging built-in capacitive signature. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. ACM, Denver, Colorado, pp 1635–1637

  35. Tuama A, Comby F, Chaumont M (2016) Camera model identification based machine learning approach with high order statistics features. In: 2016 24th European signal processing conference (EUSIPCO). IEEE, Budapest, pp 1183–1187

  36. Kurtz A, Gascon H, Becker T, Rieck K, Freiling F (2016) Fingerprinting mobile devices using personalized configurations. Proc Priv Enhanc Technol 2016(1):4–19

    Google Scholar 

  37. Baldini G, Dimc F, Kamnik R, Steri G, Giuliani R, Gentile C (2017) Identification of mobile phones using the built-in magnetometers stimulated by motion patterns. Sensors 17(4):783

    Article  Google Scholar 

  38. Miettinen M, Marchal S, Hafeez I, Asokan N, Sadeghi A-R, Tarkoma S (2017) Iot sentinel: automated device-type identification for security enforcement in IoT. In: 2017 IEEE 37th international conference on distributed computing systems (ICDCS). IEEE, Atlanta, GA, pp 2177–2184

  39. Meidan Y, Bohadana M, Shabtai A, Guarnizo JD, Ochoa M, Tippenhauer NO, Elovici Y (2017) Profiliot: a machine learning approach for IoT device identification based on network traffic analysis. In: Proceedings of the symposium on applied computing. ACM, Marrakech, Morocco, pp 506–509

  40. Kotenko I, Saenko I, Skorik F, Bushuev S (2015) Neural network approach to forecast the state of the internet of things elements. In: 2015 XVIII international conference on soft computing and measurements (SCM), May 2015, St. Petersburg, pp 133–135

  41. Baldini G, Giuliani R, Steri G, Neisse R (2017) Physical layer authentication of internet of things wireless devices through permutation and dispersion entropy. In: 2017 global internet of things summit (GIoTS), Geneva, pp 1–6

  42. Sharaf-Dabbagh Y, Saad W (2017) Demo abstract: cyber-physical fingerprinting for internet of things authentication. In: 2017 IEEE/ACM second international conference on internet-of-things design and implementation (IoTDI), Pittsburgh, PA, pp 301–302

  43. Jeong HJ, Lee HJ, Moon SM (2017) Work-in-progress: cloud-based machine learning for iot devices with better privacy. In: 2017 international conference on embedded software (EMSOFT), Seoul, pp 1–2

  44. Jincy VJ, Sundararajan S (2015) Classification mechanism for iot devices towards creating a security framework. In: Buyya R, Thampi SM (eds) Intelligent distributed computing. Springer International Publishing, Cham, pp 265–277

    Google Scholar 

  45. Nobakht M, Sivaraman V, Boreli R (2016) A host-based intrusion detection and mitigation framework for smart home iot using OpenFlow. In: 2016 11th international conference on availability, reliability and security (ARES), Salzburg, pp 147–156

  46. Caedo J, Skjellum A (2016) Using machine learning to secure IoT systems. In: 2016 14th annual conference on privacy, security and trust (PST), Auckland, pp 219–222

  47. Do VT, Engelstad P, Feng B, van Do T (2016) Strengthening mobile network security using machine learning. In: Younas M, Awan I, Kryvinska N, Strauss C, Thanh DV (eds) Mobile web and intelligent information systems. Springer International Publishing, Cham, pp 173–183

    Chapter  Google Scholar 

  48. Stroeh K, Mauro Madeira ER, Goldenstein SK (2013) An approach to the correlation of security events based on machine learning techniques. J Internet Serv Appl 4(1):7

  49. Rathore H, Jha S (2013) Bio-inspired machine learning based wireless sensor network security. In: 2013 world congress on nature and biologically inspired computing. IEEE, Fargo, ND, pp 140–146

  50. Davis A, Parikh J, Weihl WE (2004) Edge computing: extending enterprise applications to the edge of the internet. In: International conference on World Wide Web—Alternate track papers & posters, WWW 2004. ACM, New York, NY, pp 180–187

  51. Grewe D, Wagner M, Arumaithurai M, Psaras I, Kutscher D (2017) Information-centric mobile edge computing for connected vehicle environments: challenges and research directions. In: The workshop on mobile edge communications. ACM, Los Angeles, CA, pp 7–12

  52. Borthakur D, Dubey H, Constant N, Mahler L, Mankodiya K (2017) Smart fog: fog computing framework for unsupervised clustering analytics in wearable internet of things. In: 2017 IEEE global conference on signal and information processing (GlobalSIP). IEEE, Montreal, QC, pp 472–476

  53. Drolia U, Guo K, Narasimhan P (2017) Precog: prefetching for image recognition applications at the edge. In: ACM/IEEE symposium on edge computing. ACM, San Jose, California, pp 1–13

  54. Azimi I, Anzanpour A, Rahmani AM, Pahikkala T, Levorato M, Liljeberg P, Dutt N (2107) HiCH: Hierarchical fog-assisted computing architecture for healthcare IoT. ACM Trans Embed Comput Syst 16(5s):174

  55. Grassi G, Sammarco M, Bahl P, Jamieson K, Pau G (2015) Poster: Parkmaster: Leveraging edge computing in visual analytics. In: Proceedings of the 21st Annual International Conference on Mobile Computing and Networking, ser. MobiCom ’15. ACM, New York, pp 257–259. https://doi.org/10.1145/2789168.2795174 (Online)

  56. Wang S, Zhao Y, Huang L, Xu J, Hsu CH (2017) Qos prediction for service recommendations in mobile edge computing. J Parallel Distrib Comput. https://doi.org/10.1016/j.jpdc.2017.09.014

    Google Scholar 

  57. Zissis D (2017) Intelligent security on the edge of the cloud. In: International conference on engineering, technology and innovation. IEEE, Funchal, pp 1066–1070

  58. Schneible J, Lu A (2017) Anomaly detection on the edge. In: MILCOM 2017—2017 IEEE Military Communications Conference (MILCOM), pp 678–682

  59. Abeshu A, Chilamkurti N (2018) Deep learning: the frontier for distributed attack detection in fog-to-things computing. IEEE Commun Mag 56(2):169–175

    Article  Google Scholar 

  60. Yang M, Zhu T, Liu B, Xiang Y, Zhou W (2018) Machine learning differential privacy with multifunctional aggregation in a fog computing architecture. IEEE Access 6:17119–17129. https://doi.org/10.1109/ACCESS.2018.2817523

    Article  Google Scholar 

  61. Hogan M, Esposito F (2017) Stochastic delay forecasts for edge traffic engineering via bayesian networks. In: IEEE international symposium on network computing and applications. IEEE, Cambridge, MA, pp 1–4

  62. Kim H, Feamster N (2013) Improving network management with software defined networking. Commun Mag IEEE 51(2):114–119

    Article  Google Scholar 

  63. Kim HJ, Jung MY, Chin WS, Jang JW (2017) Identifying service contexts for qos support in iot service oriented software defined networks. In: Bouzefrane S, Banerjee S, Sailhan F, Boumerdassi S, Renault E (eds) Mobile, secure, and programmable networking. MSPN 2017, Lecture notes in computer science, vol 10566. Springer, Cham, pp 99–108

  64. Vukobratovic D, Jakovetic D, Skachek V, Bajovic D, Sejdinovic D, Kurt GK, Hollanti C, Fischer I (2016) Condense: a reconfigurable knowledge acquisition architecture for future 5g iot. IEEE Access 4:3360–3378

    Article  Google Scholar 

  65. Jagadeesan LJ, Mendiratta V (2016) Programming the network: application software faults in software-defined networks. In: 2016 IEEE international symposium on software reliability engineering workshops (ISSREW). IEEE, Ottawa, ON, pp 125–131

  66. Taneja M (2016) A framework for traffic management in iot networks. In: 2016 2nd international conference on contemporary computing and informatics (IC3I). IEEE, Noida, pp 316–323

  67. Uwagbole SO, Buchanan WJ, Fan L (2017) An applied pattern-driven corpus to predictive analytics in mitigating sql injection attack. In: 2017 seventh international conference on emerging security technologies (EST). IEEE, Canterbury, pp 12–17

  68. Ahmed ME, Kim H, Park M (2017) Mitigating dns query-based ddos attacks with machine learning on software-defined networking. In: MILCOM 2017—2017 IEEE military communications conference (MILCOM). IEEE, Baltimore, MD, pp 11–16

  69. Bhunia SS, Gurusamy M (2017) Dynamic attack detection and mitigation in iot using sdn. In: 2017 27th international telecommunication networks and applications conference (ITNAC). IEEE, Melbourne, VIC, pp 1–6

  70. Asthana S, Megahed A, Strong R (2017) A recommendation system for proactive health monitoring using IoT and wearable technologies. In: 2017 IEEE international conference on AI mobile services (AIMS). IEEE, Honolulu, HI, pp 14–21

  71. Walinjkar A, Woods J (2017) ECG classification and prognostic approach towards personalized healthcare. In: 2017 international conference on social media, wearable and web analytics (Social Media). IEEE, London, pp 1–8

  72. Nguyen HH, Mirza F, Naeem MA, Nguyen M (2017) A review on iot healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback. In: 2017 IEEE 21st international conference on computer supported cooperative work in design (CSCWD). IEEE, Wellington, pp 257–262

  73. Madeira R, Nunes L (2016) A machine learning approach for indirect human presence detection using IoT devices. In: 2016 eleventh international conference on digital information management (ICDIM). IEEE, Porto, pp 145–150

  74. Pandey PS (2017) Machine learning and iot for prediction and detection of stress. In: 2017 17th international conference on computational science and its applications (ICCSA). IEEE, Trieste, pp 1–5

  75. Kwapisz JR, Weiss GM, Moore SA (2011) Activity recognition using cell phone accelerometers. SIGKDD Explor Newsl 12(2):74–82

    Article  Google Scholar 

  76. Patil SS, Thorat SA (2016) Early detection of grapes diseases using machine learning and IoT. In: 2016 second international conference on cognitive computing and information processing (CCIP). IEEE, Mysore, pp 1–5

  77. Siryani J, Tanju B, Eveleigh TJ (2017) A machine learning decision support system improves the internet of things smart meter operations. IEEE Internet Things J 4(4):1056–1066

    Article  Google Scholar 

  78. Ling X, Sheng J, Baiocchi O, Liu X, Tolentino ME (2017) Identifying parking spaces detecting occupancy using vision-based IoT devices. In: 2017 global internet of things summit (GIoTS). IEEE, Geneva, pp 1–6

  79. Guo W, Fukatsu T, Ninomiya S (2015) Automated characterization of flowering dynamics in rice using field-acquired time-series rgb images. Plant Methods 11(1):7

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by the National Natural Science Foundation of China(Grant Nos. 61772345, 61402294, 61672358 and 61502314), the Major Fundamental Research Project in the Science and Technology Plan of Shenzhen(Grant Nos. JCYJ20150324140036842, JCYJ20160310095523765, JCYJ20160307111232895 and JCYJ20160307115030281), the Science and Technology Research Project of Chongqing Municipal Education Commission of China(The Research on Data Integrity Detection based on the Cloud Storage, No. KJ1601401), and a grant from Innovation and Technology Fund of Hong Kong (Project No. ITS/304/16).

Author information

Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, People’s Republic of China

    Laizhong Cui, Shu Yang, Fei Chen, Zhong Ming & Nan Lu

  2. Center for Smart Health, School of Nursing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

    Jing Qin

Authors
  1. Laizhong Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shu Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Fei Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhong Ming
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nan Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jing Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fei Chen.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, L., Yang, S., Chen, F. et al. A survey on application of machine learning for Internet of Things. Int. J. Mach. Learn. & Cyber. 9, 1399–1417 (2018). https://doi.org/10.1007/s13042-018-0834-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-018-0834-5

Keywords

Search

Navigation