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Future Internet
Special Issues
Convergence of IoT, Edge and Cloud Systems
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Convergence of IoT, Edge and Cloud Systems

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: 30 November 2025 | Viewed by 5981

Special Issue Editors

Dr. Dandan Li

E-Mail Website
Guest Editor
School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: edge intelligence; privacy-aware machine learning; cloud computing
Dr. Li Duan

E-Mail Website
Guest Editor
School of Cyberspace Science and Technology, Beijing Jiaotong University, Beijing 100044, China
Interests: system security; artificial intelligence security; network and information security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The widespread adoption of smart devices and the Internet of Things (IoT) have a significant impact on the whole world. IoT devices generate massive amounts of data. Cloud computing provides the scalability, storage, and advanced analysis capabilities required for processing and analyzing large volumes of IoT data. But, processing these data in the cloud alone can lead to issues such as high latency, network congestion, and increased costs. Fortunately, edge computing aims to address these challenges by bringing computation and storage closer to the data source, reducing the need for data transmission to the cloud. Thus, this has led to the emergence of cloud-edge IoT, which integrates IoT with cloud computing and edge computing. The hierarchical cloud-edge IoT architecture offers numerous benefits by enabling data aggregation, storage, and processing. But, achieving an effective convergence of IoT, cloud, and edge computing requires overcoming various design, implementation, deployment, security, privacy, and evaluation challenges.

The goal of this Special Issue is to provide an overview of the latest developments regarding the convergence of IoT, edge and cloud systems. Both theoretical and technical aspects are of interest. Interdisciplinary approaches are also highly welcome.

Dr. Dandan Li
Dr. Li Duan
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. Future Internet is an international peer-reviewed open access monthly 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 1600 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 computing
  • edge intelligence
  • hybrid cloud-edge architectures
  • communication protocols
  • IoT device orchestration, industry application, security and privacy

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Published Papers (3 papers)

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Research

14 pages, 522 KiB  
Article
NUDIF: A Non-Uniform Deployment Framework for Distributed Inference in Heterogeneous Edge Clusters
by Peng Li, Chen Qing and Hao Liu
Future Internet 2025, 17(4), 168; https://doi.org/10.3390/fi17040168 - 11 Apr 2025
Viewed by 228
Abstract
Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization [...] Read more.
Distributed inference in resource-constrained heterogeneous edge clusters is fundamentally limited by disparities in device capabilities and load imbalance issues. Existing methods predominantly focus on optimizing single-pipeline allocation schemes for partitioned sub-models. However, such approaches often lead to load imbalance and suboptimal resource utilization under concurrent batch processing scenarios. To address these challenges, we propose a non-uniform deployment inference framework (NUDIF), which achieves high-throughput distributed inference service by adapting to heterogeneous resources and balancing inter-stage processing capabilities. Formulated as a mixed-integer nonlinear programming (MINLP) problem, NUDIF is responsible for planning the number of instances for each sub-model and determining the specific devices for deploying these instances, while considering computational capacity, memory constraints, and communication latency. This optimization minimizes inter-stage processing discrepancies and maximizes resource utilization. Experimental evaluations demonstrate that NUDIF enhances system throughput by an average of 9.95% compared to traditional single-pipeline optimization methods under various scales of cluster device configurations. Full article
(This article belongs to the Special Issue Convergence of IoT, Edge and Cloud Systems)
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21 pages, 7852 KiB  
Article
MEC Server Status Optimization Framework for Energy Efficient MEC Systems by Taking a Deep-Learning Approach
by Minseok Koo and Jaesung Park
Future Internet 2024, 16(12), 441; https://doi.org/10.3390/fi16120441 - 28 Nov 2024
Viewed by 805
Abstract
Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the [...] Read more.
Reducing energy consumption in a MEC (Multi-Access Edge Computing) system is a critical goal, both for lowering operational expenses and promoting environmental sustainability. In this paper, we focus on the problem of managing the sleep state of MEC servers (MECSs) to decrease the overall energy consumption of a MEC system while providing users acceptable service delays. The proposed method achieves this objective through dynamic orchestration of MECS activation states based on systematic analysis of workload distribution patterns. To facilitate this optimization, we formulate the MECS sleep control mechanism as a constrained combinatorial optimization problem. To resolve the formulated problem, we take a deep-learning approach. We develop a task arrival rate predictor using a spatio-temporal graph convolution network (STGCN). We then integrate this predicted information with the queue length distribution to form the input state for our deep reinforcement learning (DRL) agent. To verify the effectiveness of our proposed framework, we conduct comprehensive simulation studies incorporating real-world operational datasets, with comparative evaluation against established metaheuristic optimization techniques. The results indicate that our method demonstrates robust performance in MECS state optimization, maintaining operational efficiency despite prediction uncertainties. Accordingly, the proposed approach yields substantial improvements in system performance metrics, including enhanced energy utilization efficiency, decreased service delay violation rate, and reduced computational latency in operational state determination. Full article
(This article belongs to the Special Issue Convergence of IoT, Edge and Cloud Systems)
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20 pages, 2522 KiB  
Article
Application of Fuzzy Logic for Horizontal Scaling in Kubernetes Environments within the Context of Edge Computing
by Sérgio N. Silva, Mateus A. S. de S. Goldbarg, Lucileide M. D. da Silva and Marcelo A. C. Fernandes
Future Internet 2024, 16(9), 316; https://doi.org/10.3390/fi16090316 - 2 Sep 2024
Cited by 2 | Viewed by 4520
Abstract
This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, [...] Read more.
This paper presents a fuzzy logic-based approach for replica scaling in a Kubernetes environment, focusing on integrating Edge Computing. The proposed FHS (Fuzzy-based Horizontal Scaling) system was compared to the standard Kubernetes scaling mechanism, HPA (Horizontal Pod Autoscaler). The comparison considered resource consumption, the number of replicas used, and adherence to latency Service-Level Agreements (SLAs). The experiments were conducted in an environment simulating Edge Computing infrastructure, with virtual machines used to represent edge nodes and traffic generated via JMeter. The results demonstrate that FHS achieves a reduction in CPU consumption, uses fewer replicas under the same stress conditions, and exhibits more distributed SLA latency violation rates compared to HPA. These results indicate that FHS offers a more efficient and customizable solution for replica scaling in Kubernetes within Edge Computing environments, contributing to both operational efficiency and service quality. Full article
(This article belongs to the Special Issue Convergence of IoT, Edge and Cloud Systems)
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