Data Mining for Internet of Things

Submission Deadline: 31 March 2020

IEEE Access invites manuscript submissions in the area of Data Mining for Internet of Things.

The Internet of Things (IoT) has become an important research domain as mature appliances, systems, infrastructures, and their applications have shown their potential in recent years. We can foresee that smart homes and smart cities using these technologies will be realized in the near future. However, many consumers have concerns with the “smart” information system and environment, especially when entering the era of IoT. The expectations of IoT and its relevant products in this new era are quite high. Instead of smartness alone, consumers of IoT products and services would like to see IoT technologies bring about more intelligent systems and environments. The main difference between the “smart thing” and “intelligent thing” is that the former will use predefined rules to provide services to a user whereas the latter will not only use predefined rules, but will also use the analytical results from intelligent mechanisms to discover suitable services for users. More precisely, using only the predefined rules may not be sufficient to consider every possible situation because the number of rules is limited. Using the results obtained after data analysis we can provide additional information to an IoT system to make it better understand the needs of a user. This is why data analytics has become a promising technology for IoT.

Although most researchers of data mining have recognized how to analyze large-scale data is an important research topic for many years, considerations for the IoT environment are quite different from those for the traditional environment because data for the IoT will be created more quickly and in different formats. That is why research on data analytics for IoT have typically been relevant to big data analytics and cloud computing technologies in recent years. This does not mean that traditional data mining and intelligent algorithms are no longer useful for IoT. In fact, how to redesign these algorithms to make them more efficiently and effectively work for IoT has been a critical research trend. In addition to modifying the traditional data mining and intelligent algorithms, an alternative is to develop new data analysis algorithms. Using deep learning technologies for supervised learning to construct a set of classifiers to recognize data entering an IoT system, and using metaheuristic algorithms for unsupervised learning to find out good solutions for classifying unknown data are two promising technologies today. Moreover, how to determine interesting patterns from a series of events of an IoT system is also a critical research topic. In summary, many modern technologies, such as big data analytics, statistical technologies, and other analysis technologies, have also been used for finding out useful information from an IoT system to provide needed services to a user and to enhance the performance of the IoT system as a whole today.

This Special Section in IEEE Access will focus on data mining technologies for the IoT and its applications, such as smart home, smart city, industry, online social network, and even internet of vehicles. We also welcome research on IoT related technologies, such as cloud computing, network security, wireless sensor network, vehicular networking, smart grids, and big data.

The topics of interest include, but are not limited to:

  • Data Mining for the IoT
  • Machine and Deep Learning for the IoT
  • Metaheuristic Algorithms for the IoT
  • Cloud Computing for the IoT
  • Big Data for the IoT
  • Mobile Computing and Sensing for the IoT
  • Security Framework for the IoT
  • Privacy Protection for the IoT
  • IoT in Smart Home and Smart City
  • IoT in Energy Management
  • Industry IoT
  • IoT in Agriculture and Environment
  • IoT in eHealth and Ambient Assisted Living
  • Internet of Vehicles
  • Edge Computing
  • Applications of the IoT


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Associate Editor: Chun-Wei Tsai, National Chung-Hsing University, Taiwan

Guest Editors:

  1. Mu-Yen Chen, National Taichung University of Science and Technology, Taiwan
  2. Francesco Piccialli, University of Naples Federico II, Italy
  3. Tie Qiu, Tianjin University, China
  4. Jason J. Jung, Chung-Ang University, Republic of Korea
  5. Patrick C. K. Hung, University of Ontario Institute of Technology, Canada
  6. Sherali Zeadally, University of Kentucky, USA


Relevant IEEE Access Special Sections:

  1. Smart Caching, Communications, Computing and Cybersecurity for Information-Centric Internet of Things
  2. Healthcare Information Technology for the Extreme and Remote Environments
  3. Internet-of-Things (IoT) Big Data Trust Management

IEEE Access Editor-in-Chief:
Michael Pecht, Professor and Director, CALCE, University of Maryland

Paper submission: Contact Associate Editor and submit manuscript to:

For inquiries regarding this Special Section, please contact: