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Future Internet
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Cloud/Edge Computing for Next-Generation Networks: Architecture and Applications
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Cloud/Edge Computing for Next-Generation Networks: Architecture and Applications

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

Deadline for manuscript submissions: 31 August 2026 | Viewed by 7262

Special Issue Editor

Dr. Lav Gupta

E-Mail Website
Guest Editor
Department of Computer Science, University of Missouri—St. Louis, St. Louis, MO 63121, USA
Interests: networking; cloud computing; 5G/6G cellular networks; cybersecurity; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid evolution of next-generation networks requires intelligent, flexible, and high-performance solutions to manage massive volumes of data and meet diverse computational demands. The combination of cloud and edge computing enables seamless integration between centralized and distributed processing, powering applications such as the industrial IoT, AI systems, smart cities, smart homes, and advanced mobile services. While this integration opens up transformative opportunities, it also poses challenges in areas such as system architecture, controllability, data flow optimization, and security.

This Special Issue aims to promote advances in cloud and edge computing for next-generation networks. We invite research contributions that propose, evaluate, or review innovative architectures, applications, and frameworks that focus on such critical challenges and unlock the full potential of these technologies.

The scope of this Special Issue includes, but is not limited to, the following topics:

  • Frameworks for integrating cloud and edge computing.
  • Resource allocation and scheduling in cloud-edge systems.
  • Security and privacy solutions for cloud-edge networks.
  • AI/ML-driven extensions for cloud-edge environments.
  • Interoperability in heterogeneous cloud-edge systems.
  • Energy-efficient designs for cloud-edge infrastructures.
  • Deployment strategies for cloud edges in 5G/6G networks.
  • Cloud-edge applications across industries (e.g., healthcare, smart cities, and smart homes).
  • Latency-critical use cases (e.g., autonomous vehicles, automation).
  • Reliability and fault tolerance in distributed cloud-edge ecosystems.

Dr. Lav Gupta
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 250 words) can be sent to the Editorial Office for assessment.

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 1800 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 computing
  • 5G/6G networks
  • cloud-edge applications
  • cloud-edge systems

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Published Papers (3 papers)

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Research

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42 pages, 5531 KB  
Article
DRL-TinyEdge: Energy- and Latency-Aware Deep Reinforcement Learning for Adaptive TinyML at the 6G Edge
by Saad Alaklabi and Saleh Alharbi
Future Internet 2026, 18(1), 31; https://doi.org/10.3390/fi18010031 - 4 Jan 2026
Viewed by 1896
Abstract
Various TinyML models face a constantly challenging environment when running on emerging sixth-generation (6G) edge networks, with volatile wireless environments, limited computing power, and highly constrained energy use. This paper introduces DRL-TinyEdge, a latency- and energy-sensitive deep reinforcement learning (DRL) platform optimised for [...] Read more.
Various TinyML models face a constantly challenging environment when running on emerging sixth-generation (6G) edge networks, with volatile wireless environments, limited computing power, and highly constrained energy use. This paper introduces DRL-TinyEdge, a latency- and energy-sensitive deep reinforcement learning (DRL) platform optimised for the 6G edge of adaptive TinyML. The suggested on-device DRL controller autonomously decides on the execution venue (local, partial, or cloud) and model configuration (depth, quantization, and frequency) in real time to trade off accuracy, latency, and power savings. To assure safety during adaptation to changing conditions, the multi-objective reward will be a combination of p95 latency, per-inference energy, preservation of accuracy and policy stability. The system is tested under two workloads representative of classical applications, including image classification (CIFAR-10) and sensor analytics in an industrial IoT system, on a low-power platform (ESP32, Jetson Nano) connected to a simulated 6G mmWave testbed. Findings indicate uniform improvements, with up to a 28 per cent decrease in p95 latency and a 43 per cent decrease in energy per inference, and with accuracy differences of less than 1 per cent compared to baseline models. DRL-TinyEdge offers better adaptability, stability, and scalability when using a CPU < 5 and a decision latency < 10 ms, compared to Static-Offload, Heuristic-QoS, or TinyNAS/QAT. Code, hyperparameter settings, and measurement programmes will also be published at the time of acceptance to enable reproducibility and open benchmarking. Full article
(This article belongs to the Special Issue Cloud/Edge Computing for Next-Generation Networks: Architecture and Applications)
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27 pages, 5632 KB  
Article
Semantic Fusion of Health Data: Implementing a Federated Virtualized Knowledge Graph Framework Leveraging Ontop System
by Abid Ali Fareedi, Stephane Gagnon, Ahmad Ghazawneh and Raul Valverde
Future Internet 2025, 17(6), 245; https://doi.org/10.3390/fi17060245 - 30 May 2025
Cited by 1 | Viewed by 2546
Abstract
Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from [...] Read more.
Data integration (DI) and semantic interoperability (SI) are critical in healthcare, enabling seamless, patient-centric data sharing across systems to meet the demand for instant, unambiguous access to health information. Federated information systems (FIS) highlight auspicious issues for seamless DI and SI stemming from diverse data sources or models. We present a hybrid ontology-based design science research engineering (ODSRE) methodology that combines design science activities with ontology engineering principles to address the above-mentioned issues. The ODSRE constructs a systematic mechanism leveraging the Ontop virtual paradigm to establish a state-of-the-art federated virtual knowledge graph framework (FVKG) embedded virtualized knowledge graph approach to mitigate the aforementioned challenges effectively. The proposed FVKG helps construct a virtualized data federation leveraging the Ontop semantic query engine that effectively resolves data bottlenecks. Using a virtualized technique, the FVKG helps to reduce data migration, ensures low latency and dynamic freshness, and facilitates real-time access while upholding integrity and coherence throughout the federation system. As a result, we suggest a customized framework for constructing ontological monolithic semantic artifacts, especially in FIS. The proposed FVKG incorporates ontology-based data access (OBDA) to build a monolithic virtualized repository that integrates various ontological-driven artifacts and ensures semantic alignments using schema mapping techniques. Full article
(This article belongs to the Special Issue Cloud/Edge Computing for Next-Generation Networks: Architecture and Applications)
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Review

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39 pages, 2037 KB  
Review
Research on Intelligent Resource Management Solutions for Green Cloud Computing
by Amirmohammad Parhizkar, Ehsan Arianyan and Pejman Goudarzi
Future Internet 2026, 18(2), 76; https://doi.org/10.3390/fi18020076 - 1 Feb 2026
Viewed by 1560
Abstract
Cloud computing utilization has experienced progressive expansion over the last decade, which has raised concerns and challenges regarding efficient resource allocation and energy efficiency. The burgeoning increase in the number of cloud computing users and their data exacerbates the difficulty of resolving these [...] Read more.
Cloud computing utilization has experienced progressive expansion over the last decade, which has raised concerns and challenges regarding efficient resource allocation and energy efficiency. The burgeoning increase in the number of cloud computing users and their data exacerbates the difficulty of resolving these challenges using conventional methods. Thus, utilizing intelligent approaches is indispensable. Among the most recent intelligent methods, artificial intelligence-based techniques have gained prominence across numerous research domains, including cloud resource management. Through a literature review aimed at analyzing existing studies addressing the open challenges of cloud computing, we have identified some gaps that are presented in this paper. Moreover, this paper presents a survey on cloud resource management solutions spanning from 2018 to 2025, with a focus on the papers that utilized intelligent methodologies for green computing. More specifically, this study shed light on the prevailing challenges in the field concerning methods, research areas, metrics, tools, and datasets. Furthermore, it provides a clear classification of methods, research areas, and metrics. Full article
(This article belongs to the Special Issue Cloud/Edge Computing for Next-Generation Networks: Architecture and Applications)
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