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
Summary
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.
Keywords
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
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