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Cloud and Edge Computing for IoT Applications
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Cloud and Edge Computing for IoT Applications

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

Deadline for manuscript submissions: 20 March 2025 | Viewed by 4190

Special Issue Editors

Dr. Haoran Chi

E-Mail Website
Guest Editor
Instituto de Telecomunicações and Universidade de Aveiro, Campus Universitário de Santiago, 3810-193 Aveiro, Portugal
Interests: 5G/6G; cloud/edge computing; network orchestration and optimization; machine learning; eHealth
Special Issues, Collections and Topics in MDPI journals
Dr. Rutvij Jhaveri

E-Mail Website
Guest Editor
Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, India
Interests: network security/resilience; SDN; CPS/IoT systems; routing challenges
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Nowadays, our society has been experiencing the development from 5G towards 5G beyond 6G, focusing on digitalization by utilizing the cloud/fog computing-based Internet of Things (IoT). To aid the evolution of the future Internet of Everything (IoE), full utilization of cloud and edge computing provides powerful computation resources for artificial intelligence (AI)-based big data analysis at the cyber level. The popularization of cloud computing in 4G/5G boosts the evolution of computing networks towards smart applications, relying on its high computation/storage capacity. Meanwhile, the success of edge computing in 5G provides close-to-user computation/storage capacities, which not only offload the excessive amount of data in cloud computing but are also capable of fulfilling the low latency requirement of many IoT applications. However, to fulfill the conceived requirement of 6G, current cloud and edge computing technologies embrace emerging challenges regarding the densification of IoT devices, high heterogeneousness, supporting pervasive artificial intelligence, energy efficiency, privacy/security preservation, etc.

Therefore, this Special Issue focuses on tackling the corresponding problems for cloud and edge computing to support IoT applications towards 6G, considering the emerging challenges and opportunities arisen by the cutting-edge communication and computing technologies. We encourage papers in all areas related to this topic, e.g., edge/cloud computing, softwarization, IoT systems and applications, etc. The papers solicited by this Special Issue cover numerous topics of interest that include, but are not limited to, the following:

  • Theoretical modeling, analysis, and development for cloud and edge computing networks;
  • Cloud/edge-based softwarization and virtualization;
  • Embedded AI in cloud and edge computing for IoT applications;
  • AI-enabled algorithms for cloud and edge computing;
  • Cloud and edge computing-based data analysis for IoT applications;
  • Security and privacy strategies for cloud/edge computing and the IoT;
  • Emerging applications related to cloud/edge computing for the IoT in 5G/6G;
  • Integrated testbed and case studies for cloud/edge computing for IoT applications.

Dr. Haoran Chi
Dr. Rutvij Jhaveri
Guest Editors

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
  • embedded AI
  • network security
  • IoT
  • 5G/6G

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Published Papers (2 papers)

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21 pages, 818 KiB  
Article
Auto-Scaling Techniques in Cloud Computing: Issues and Research Directions
by Saleha Alharthi, Afra Alshamsi, Anoud Alseiari and Abdulmalik Alwarafy
Sensors 2024, 24(17), 5551; https://doi.org/10.3390/s24175551 - 28 Aug 2024
Cited by 1 | Viewed by 2562
Abstract
In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental [...] Read more.
In the dynamic world of cloud computing, auto-scaling stands as a beacon of efficiency, dynamically aligning resources with fluctuating demands. This paper presents a comprehensive review of auto-scaling techniques, highlighting significant advancements and persisting challenges in the field. First, we overview the fundamental principles and mechanisms of auto-scaling, including its role in improving cost efficiency, performance, and energy consumption in cloud services. We then discuss various strategies employed in auto-scaling, ranging from threshold-based rules and queuing theory to sophisticated machine learning and time series analysis approaches. After that, we explore the critical issues in auto-scaling practices and review several studies that demonstrate how these challenges can be addressed. We then conclude by offering insights into several promising research directions, emphasizing the development of predictive scaling mechanisms and the integration of advanced machine learning techniques to achieve more effective and efficient auto-scaling solutions. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for IoT Applications)
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Review

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17 pages, 3807 KiB  
Review
A Survey on IoT Application Architectures
by Abdulkadir Dauda, Olivier Flauzac and Florent Nolot
Sensors 2024, 24(16), 5320; https://doi.org/10.3390/s24165320 - 17 Aug 2024
Viewed by 1269
Abstract
The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems’ deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures [...] Read more.
The proliferation of the IoT has led to the development of diverse application architectures to optimize IoT systems’ deployment, operation, and maintenance. This survey provides a comprehensive overview of the existing IoT application architectures, highlighting their key features, strengths, and limitations. The architectures are categorized based on their deployment models, such as cloud, edge, and fog computing approaches, each offering distinct advantages regarding scalability, latency, and resource efficiency. Cloud architectures leverage centralized data processing and storage capabilities to support large-scale IoT applications but often suffer from high latency and bandwidth constraints. Edge architectures mitigate these issues by bringing computation closer to the data source, enhancing real-time processing, and reducing network congestion. Fog architectures combine the strengths of both cloud and edge paradigms, offering a balanced solution for complex IoT environments. This survey also examines emerging trends and technologies in IoT application management, such as the solutions provided by the major IoT service providers like Intel, AWS, Microsoft Azure, and GCP. Through this study, the survey identifies latency, privacy, and deployment difficulties as key areas for future research. It highlights the need to advance IoT Edge architectures to reduce network traffic, improve data privacy, and enhance interoperability by developing multi-application and multi-protocol edge gateways for efficient IoT application management. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for IoT Applications)
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