Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Cloud and Edge Computing for IoT Applications
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 25 February 2026 | Viewed by 13090

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

16 pages, 13024 KiB  
Article
Edge Computing Based on Convolutional Neural Network for Passenger Counting: A Case Study in Guadalajara, Mexico
by Roxana Sánchez Laguna, Ulises Davalos-Guzman and Lina M. Aguilar-Lobo
Sensors 2025, 25(6), 1695; https://doi.org/10.3390/s25061695 - 9 Mar 2025
Viewed by 702
Abstract
One of the most common deficiencies in the public transport system is long waiting times. Currently, in the Guadalajara Metropolitan Area, Mexico, the frequencies of routes are fixed, making it impossible to satisfy a demand with a dynamic variation. An intelligent public transport [...] Read more.
One of the most common deficiencies in the public transport system is long waiting times. Currently, in the Guadalajara Metropolitan Area, Mexico, the frequencies of routes are fixed, making it impossible to satisfy a demand with a dynamic variation. An intelligent public transport system is required. The first step to solve this problem is knowing the number of users so that we can respond appropriately to each scenario. In this context, this work focuses on the design and implementation of an embedded system module for passenger counting that can be used to improves public transport service quality. This work presents three contributions. First, a design and experimental validation of the passenger counting system is presented to determine the number of users in an image and send this information to a server suitable for the public transportation system in Guadalajara, Mexico. Second, the generation of two new datasets is reported for training and testing the CSRNet algorithm with images of public transportation systems in Mexican cities. Finally, we make the hardware implementation of the passenger counting system in a Jetson Nano development board. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for IoT Applications)
►▼ Show Figures

Figure 1

24 pages, 5379 KiB  
Article
A Novel Orchestrator Architecture for Deploying Virtualized Services in Next-Generation IoT Computing Ecosystems
by Francisco Mahedero Biot, Alejandro Fornes-Leal, Rafael Vaño, Raúl Reinosa Simón, Ignacio Lacalle, Carlos Guardiola and Carlos E. Palau
Sensors 2025, 25(3), 718; https://doi.org/10.3390/s25030718 - 24 Jan 2025
Viewed by 680
Abstract
The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native [...] Read more.
The Next-Generation IoT integrates diverse technological enablers, allowing the creation of advanced systems with increasingly complex requirements and maximizing the use of available IoT–edge–cloud resources. This paper introduces an orchestrator architecture for dynamic IoT scenarios, inspired by ETSI NFV MANO and Cloud Native principles, where distributed computing nodes often have unfixed and changing networking configurations. Unlike traditional approaches, this architecture also focuses on managing services across massively distributed mobile nodes, as demonstrated in the automotive use case presented. Apart from working as MANO framework, the proposed solution efficiently handles service lifecycle management in large fleets of vehicles without relying on public or static IP addresses for connectivity. Its modular, microservices-based approach ensures adaptability to emerging trends like Edge Native, WebAssembly and RISC-V, positioning it as a forward-looking innovation for IoT ecosystems. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for IoT Applications)
►▼ Show Figures

Figure 1

20 pages, 7204 KiB  
Article
Virtual Node-Driven Cloud–Edge Collaborative Resource Scheduling for Surveillance with Visual Sensors
by Xinyang Gu, Zhansheng Duan, Guangyuan Ye and Zhenjun Chang
Sensors 2025, 25(2), 535; https://doi.org/10.3390/s25020535 - 17 Jan 2025
Viewed by 716
Abstract
For public security purposes, distributed surveillance systems are widely deployed in key areas. These systems comprise visual sensors, edge computing boxes, and cloud servers. Resource scheduling algorithms are critical to ensure such systems’ robustness and efficiency. They balance workloads and need to meet [...] Read more.
For public security purposes, distributed surveillance systems are widely deployed in key areas. These systems comprise visual sensors, edge computing boxes, and cloud servers. Resource scheduling algorithms are critical to ensure such systems’ robustness and efficiency. They balance workloads and need to meet real-time monitoring and emergency response requirements. Existing works have primarily focused on optimizing Quality of Service (QoS), latency, and energy consumption in edge computing under resource constraints. However, the issue of task congestion due to insufficient physical resources has been rarely investigated. In this paper, we tackle the challenges posed by large workloads and limited resources in the context of surveillance with visual sensors. First, we introduce the concept of virtual nodes for managing resource shortages, referred to as virtual node-driven resource scheduling. Then, we propose a convex-objective integer linear programming (ILP) model based on this concept and demonstrate its efficiency. Additionally, we propose three alternative virtual node-driven scheduling algorithms, the extension of a random algorithm, a genetic algorithm, and a heuristic algorithm, respectively. These algorithms serve as benchmarks for comparison with the proposed ILP model. Experimental results show that all the scheduling algorithms can effectively address the challenge of offloading multiple priority tasks under resource constraints. Furthermore, the ILP model shows the best scheduling performance among them. Full article
(This article belongs to the Special Issue Cloud and Edge Computing for IoT Applications)
►▼ Show Figures

Figure 1

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 5 | Viewed by 7031
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)
►▼ Show Figures

Figure 1

Review

Jump to: Research

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
Cited by 1 | Viewed by 3132
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop