A survey on application of machine learning for Internet of Things

  • Laizhong Cui
  • Shu Yang
  • Fei ChenEmail author
  • Zhong Ming
  • Nan Lu
  • Jing Qin
Original Article


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.


Machine learning IoT Networking Application 



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).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenPeople’s Republic of China
  2. 2.Center for Smart Health, School of NursingThe Hong Kong Polytechnic UniversityKowloonHong Kong

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