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Cloud and Edge Computing for the Internet of Things
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Cloud and Edge Computing for the Internet of Things

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 January 2024) | Viewed by 3526

Special Issue Editor

Prof. Dr. Zhengwei Qi

E-Mail Website
Guest Editor
School of Software, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: cloud computing; virtualization; computer games; quality of experience; resource allocation; storage management; virtual machines; Internet

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) has revolutionized our interaction by connecting various devices and enabling seamless data exchange. As the IoT continues to evolve, the demand for efficient and ubiquitous Cloud–edge collaboration computing architectures becomes increasingly critical. This Special Issue aims to explore the intersection of cloud and edge computing within the context of the IoT, with a focus on virtualization, ubiquitous computing, and cloud–edge collaboration.

We invite researchers and practitioners to contribute original research articles, case studies, and review papers that address the challenges and opportunities presented by cloud and edge computing for the Internet of Things. Topics of interest include, but are not limited to:

  • Virtualization techniques for IoT systems;
  • Scalable architectures for ubiquitous computing;
  • Resource management and optimization;
  • Modeling heterogeneous resources;
  • Edge intelligence and machine learning;
  • Security and privacy in cloud–edge environments;
  • Smart cloud–edge collaboration strategies;
  • Hybrid computing frameworks and platforms;
  • Energy-efficient algorithms and protocols; 
  • Integration of cloud and edge computing; 
  • Case studies and real-world deployments.  

Prof. Dr. Zhengwei Qi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cloud and edge computing
  • Internet of Things
  • virtualization
  • ubiquitous computing
  • cloud–edge collaboration

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

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Research

31 pages, 8985 KiB  
Article
Novel Machine Learning Approach for DDoS Cloud Detection: Bayesian-Based CNN and Data Fusion Enhancements
by Ibtihal AlSaleh, Aida Al-Samawi and Liyth Nissirat
Sensors 2024, 24(5), 1418; https://doi.org/10.3390/s24051418 - 22 Feb 2024
Cited by 13 | Viewed by 2933
Abstract
Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% [...] Read more.
Cloud computing has revolutionized the information technology landscape, offering businesses the flexibility to adapt to diverse business models without the need for costly on-site servers and network infrastructure. A recent survey reveals that 95% of enterprises have already embraced cloud technology, with 79% of their workloads migrating to cloud environments. However, the deployment of cloud technology introduces significant cybersecurity risks, including network security vulnerabilities, data access control challenges, and the ever-looming threat of cyber-attacks such as Distributed Denial of Service (DDoS) attacks, which pose substantial risks to both cloud and network security. While Intrusion Detection Systems (IDS) have traditionally been employed for DDoS attack detection, prior studies have been constrained by various limitations. In response to these challenges, we present an innovative machine learning approach for DDoS cloud detection, known as the Bayesian-based Convolutional Neural Network (BaysCNN) model. Leveraging the CICDDoS2019 dataset, which encompasses 88 features, we employ Principal Component Analysis (PCA) for dimensionality reduction. Our BaysCNN model comprises 19 layers of analysis, forming the basis for training and validation. Our experimental findings conclusively demonstrate that the BaysCNN model significantly enhances the accuracy of DDoS cloud detection, achieving an impressive average accuracy rate of 99.66% across 13 multi-class attacks. To further elevate the model’s performance, we introduce the Data Fusion BaysFusCNN approach, encompassing 27 layers. By leveraging Bayesian methods to estimate uncertainties and integrating features from multiple sources, this approach attains an even higher average accuracy of 99.79% across the same 13 multi-class attacks. Our proposed methodology not only offers valuable insights for the development of robust machine learning-based intrusion detection systems but also enhances the reliability and scalability of IDS in cloud computing environments. This empowers organizations to proactively mitigate security risks and fortify their defenses against malicious cyber-attacks. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for the Internet of Things)
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