Table of Content

Data Science in Ubiquitous Computing: Data Analytics, Data Mining and Data Security

Submission Deadline: 31 January 2022 (closed)

Guest Editors

Dr. Chien-Ming Chen, Shandong University of Science and Technology, China.
Dr. Mohammad Mehedi Hassan, King Saud University, Saudi Arabia.
Dr. Muhammad Asghar khan, Hamdard University, Pakistan.
Dr. Hu Xiong, University of Electronic Science and Technology of China, China.


There has recently been an increasing emphasis on ubiquitous computing, including theories, methodologies, and techniques. Ubiquitous computing is to provide computing and communication services anytime and anywhere. It embeds computational capability into daily objects to effectively communicate and perform practical tasks to minimize the end user's need to interact with computers.


With the significant development of ubiquitous computing, massive raw data are collected from various environments and applications, including social networking, smart manufacturing, financial technologies, search engines, IoT, etc. These data can be related to user behavior, transaction records, the characteristics of products, production lines, etc. The size of data is increasingly grown, leading to an important issue: discovering value from big raw data.


Data science is an interdisciplinary field that combines statistics, scientific methods, mathematics, and algorithms to extract knowledge and meaningful insights from structured and unstructured data. As an emerging discipline, data science aggregates several research topics, including data analytics, data security and privacy, data mining, databases, etc.

Data science is one of the most exciting fields out there today. This Special Issue emphasizes the cutting-edge research of data science for ubiquitous computing. We encourage researchers to share their solutions in data analysis, data security and privacy, data mining, information fusion, knowledge discovery, information aggregation, etc.


Topics of interest include but are not limited to the ones listed below.

1. Data analysis for ubiquitous computing

2. Data security and privacy for ubiquitous computing

3. Data mining for ubiquitous computing

4. Information Fusion for ubiquitous computing

5. Information aggregation for ubiquitous computing

6. Big data analytics techniques and models

7. Trust management in data analytics

8. Access control and authorization


Data science, ubiquitous computing, data analytics, data mining, data security

Published Papers

  • Open Access


    Convergence Track Based Adaptive Differential Evolution Algorithm (CTbADE)

    Qamar Abbas, Khalid Mahmood Malik, Abdul Khader Jilani Saudagar, Muhammad Badruddin Khan, Mozaherul Hoque Abul Hasanat, Abdullah AlTameem, Mohammed AlKhathami
    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 1229-1250, 2022, DOI:10.32604/cmc.2022.024211
    (This article belongs to this Special Issue: Data Science in Ubiquitous Computing: Data Analytics, Data Mining and Data Security)
    Abstract One of the challenging problems with evolutionary computing algorithms is to maintain the balance between exploration and exploitation capability in order to search global optima. A novel convergence track based adaptive differential evolution (CTbADE) algorithm is presented in this research paper. The crossover rate and mutation probability parameters in a differential evolution algorithm have a significant role in searching global optima. A more diverse population improves the global searching capability and helps to escape from the local optima problem. Tracking the convergence path over time helps enhance the searching speed of a differential evolution algorithm for varying problems. An adaptive… More >

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