Guest Editors
Prof. Yaser Daanial Khan, University of Management and Technology, Pakistan.
Dr. Hao Lin, University of Electronic Science and Technology of China, China.
Dr. Dhanapal Durai Dominic, University of Technology Tronoh, Perak, Malaysia.
Summary
Data mining and supervised learning open new avenues for collaborative studies and commercial implementations. In this editorial, people can provide a thorough overview of the most recent work on perimeter cloud computing that is deeply effective for teaching. Users even offer perspectives on using computational intelligence advancements to support perimeter implementations from four separate subject areas, including intelligent visuals, transportation, and transport systems, and compete effectively. They also draw attention to the main issues and possible future lines of investigation. Humans think that this analysis will encourage thorough investigation and participation in these processes are successful. Several sensors will simultaneously produce immense quantities of useful information at the edge nodes, necessitating both quick signal collection and cognitive analysis of the data to actualize the possibilities of big boundary data. Because of their high bandwidth and constrained processing power, conventional cloud technology and computation can adequately handle this Issue.
The awesome thing is that the situation is clarified by the growing computing, which moves data processing from the network's central core to the localized edge devices, noticeably decreasing latencies and improving capacity. In addition, the latest developments in pattern recognition have significantly improved computational resources, permitting the exhilarating growth of revolutionary technologies like automated vehicles and surveillance footage. The number of end nodes and the data collected from the boundary has been expanding fast in recent years due to the web of things and the growing availability of cellular connections. In this instance, the centralized cloud-based treatment option is ineffective for handling the data produced by the perimeter.
The internet companies' ability to generate revenue is made possible by the centralized information paradigm, which downloads all information to the network infrastructure via the network and utilizes its computational capability to address storage and compute issues. Unfortunately, conventional virtualization has several drawbacks in the IoT setting. With the expanding Internet of everything, more connected phones are becoming online and producing huge amounts of data. This has resulted in issues with conventional cloud computing approaches like resource strain, sluggish processing times, lack of basic amenities, and poor confidentiality. Future IoT solutions have evolved because traditional cloud storage cannot consistently serve the diversified information processing requirements of today's politically enlightened society. It is a fresh approach for doing computations at the site's perimeter.
In light of the preceding, we invite academics to submit original research articles and review papers for the current Special Issue that will focus on using edge computing in Internet of Things devices for data sourcing.
Keywords
Research papers that consider the following questions and themes are welcomed:
1. A relevant analysis of pattern recognition for edge computing environments.
2. A survey of computation offloading systems and methodologies.
3. Internet of things comprehensive technologies and solutions for the hybrid edge of the network.
4. Lightweight emulation can be used to centralize network edge operations.
5. Presentations and analysis of protected computing in the IoT network.
6. Replication of virtual machines horizontally and vertically in the framework of cloud computing.
7. Perception and problems of the virtualized edge of the network.
8. Network edge computing's fundamental capabilities and difficulties.
9. Distributed processing method that protects confidentiality for the Internet of things aided by distributed systems.
10. Virtualization could benefit from using fogging technology to save electricity.
11. An exploration of designing and developing for sizable, adaptive computing.
12. Automation can be utilized to accelerate cloud operations.
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