Special lssues
Table of Content

Data Science for the Internet of Things

Submission Deadline: 30 October 2022 (closed)

Guest Editors

Dr. Mohammad Tabrez Quasim, University of Bisha, Saudi Arabia.
Dr. Fahad Alqarni, University of Bisha, Saudi Arabia.
Dr. Asadullah Shaikh, Najran University, Saudi Arabia.
Dr. Shakir Khan, Imam Mohammad Ibn Saud Islamic University, Saudi Arabia.
Dr. Kapal Dev, University of Johannesburg South Africa.


Data Science is gaining tremendous popularity in business world. It has an enormous effect on improving business productivity and performance. Data science can be defined as an interdisciplinary field involving techniques to collect, store, analyze, manage and publish data. In the Internet of things (IoT), different connected sensors measuring environmental parameters and generating user interaction data. Due to the increase popularity of IoT there are big flow of data predicted in coming days. The flourishing in data is not only going to require better infrastructure but smarter data science approaches. Data science techniques have been adopted to improve the IoT in terms of data throughput, self-optimization and self-management. In fact, incorporating the lifecycle proposed by the data science will impact the future of the IoT, allowing researchers to reproduce scenarios, optimize the collection, analysis and visualization of the data acquired by the IoT. Data science for IoT can help overcome some global challenges, generating more accurate decisions. Data science also allow integrating artificial intelligence; the processing of data will become easier as devices will be able to self-learn about identifying patterns. The opportunities that can be exploited using IoT data science are growing more and more. With the current trend, IoT is one of the forerunners in data generation and this is exactly why data science will be required in IoT more than ever. One of the next key challenges will be integrating the IoT and Data Science.


• Management of IoT devices based on data knowledge
• Data-centric simulations of the IoT
• Methods for assessing IoT data quality
• Standards for IoT data discovery
• IoT Data Analytics
• Machine Learning for IoT
• Integrating IoT data with external data sources
• Data Science approaches for Smart Cities
• Data Science applications and services
• IoT application Orchestration
• IoT application in the Health sector

Published Papers

  • Open Access


    Exploiting Data Science for Measuring the Performance of Technology Stocks

    Tahir Sher, Abdul Rehman, Dongsun Kim, Imran Ihsan
    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2979-2995, 2023, DOI:10.32604/cmc.2023.036553
    (This article belongs to the Special Issue: Data Science for the Internet of Things)
    Abstract The rise or fall of the stock markets directly affects investors’ interest and loyalty. Therefore, it is necessary to measure the performance of stocks in the market in advance to prevent our assets from suffering significant losses. In our proposed study, six supervised machine learning (ML) strategies and deep learning (DL) models with long short-term memory (LSTM) of data science was deployed for thorough analysis and measurement of the performance of the technology stocks. Under discussion are Apple Inc. (AAPL), Microsoft Corporation (MSFT), Broadcom Inc., Taiwan Semiconductor Manufacturing Company Limited (TSM), NVIDIA Corporation (NVDA), and… More >

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