Special lssues

Role of Big Data Management, Machine Learning, and Deep Learning Techniques for Ubiquitous Computing

Submission Deadline: 31 August 2023 (closed)

Guest Editors

Dr. Nawab Muhammad Faseeh Qureshi, Sungkyunkwan University, Korea
Dr. Isma Farah Siddiqui, Mehran University of Engineering and Technology, Pakistan
Dr. Muhammad Aslam Jarwar, Sheffield Hallam University, UK


Ubiquitous computing facilitates industrial systems through devices, data, and communication channels for solving issues anytime and everywhere. Nowadays, this technique uses the Internet of Things (IoT), Big data management, Machine learning, and Deep learning together to efficiently process solutions in the distributed systems environment. Since the nature of IoT problems has transformed from homogeneous to heterogeneous, it requires a convergence-based solution involving big data, machine learning, deep learning, and the internet of things at the same time. To address heterogeneous issues of IoT devices through big data management, machine learning, and deep learning, we require convergence-based algorithms and techniques to analyze its sub-layers, such as cloud, edge, and device, together in the distributed computing environment.


This special issue seeks conceptual, empirical, or technological papers that will offer new insights into the following topics but is not limited to them:
- Predictive, prescriptive, descriptive analytics for IoT device issues
- Programmable Ubiquitous approaches for delivering IoT device solutions
- Machine learning algorithms for addressing heterogeneous IoT devices problems
- Big data management techniques for rectifying IoT devices heterogeneous issues
- Deep learning techniques for identifying micro issues in heterogeneous IoT devices
- Embedded solutions for heterogeneous IoT device problems
- Hardware Abstraction Layer logs analytics for solving heterogeneous IoT device problems
- Network processing problems in the heterogeneous IoT devices
- Security issues in the heterogeneous IoT devices

Published Papers

  • Open Access


    Feature Selection for Detecting ICMPv6-Based DDoS Attacks Using Binary Flower Pollination Algorithm

    Adnan Hasan Bdair Aighuraibawi, Selvakumar Manickam, Rosni Abdullah, Zaid Abdi Alkareem Alyasseri, Ayman Khallel, Dilovan Asaad Zebari, Hussam Mohammed Jasim, Mazin Mohammed Abed, Zainb Hussein Arif
    Computer Systems Science and Engineering, Vol.47, No.1, pp. 553-574, 2023, DOI:10.32604/csse.2023.037948
    (This article belongs to this Special Issue: Role of Big Data Management, Machine Learning, and Deep Learning Techniques for Ubiquitous Computing)
    Abstract Internet Protocol version 6 (IPv6) is the latest version of IP that goal to host 3.4 × 1038 unique IP addresses of devices in the network. IPv6 has introduced new features like Neighbour Discovery Protocol (NDP) and Address Auto-configuration Scheme. IPv6 needed several protocols like the Address Auto-configuration Scheme and Internet Control Message Protocol (ICMPv6). IPv6 is vulnerable to numerous attacks like Denial of Service (DoS) and Distributed Denial of Service (DDoS) which is one of the most dangerous attacks executed through ICMPv6 messages that impose security and financial implications. Therefore, an Intrusion Detection System (IDS) is a monitoring system… More >

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