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Future Internet
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Intelligent Edge Computing: Leveraging Soft Computing and Machine Learning for...
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Intelligent Edge Computing: Leveraging Soft Computing and Machine Learning for Next-Generation Network Applications

A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Network Virtualization and Edge/Fog Computing".

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1191

Special Issue Editors

Prof. Dr. Giovanni Pau

E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Interests: wireless sensor networks; intelligent transportation systems; Internet of things; green communications; fuzzy logic
Special Issues, Collections and Topics in MDPI journals
Dr. Fabio Arena

E-Mail Website
Guest Editor
Faculty of Engineering and Architecture, Kore University of Enna, 94100 Enna, Italy
Interests: intelligent systems for the management of road traffic and transport infrastructure networks; automated vehicle systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to explore the intersection of edge computing, soft computing, and machine learning to enhance the capabilities and performance of next-generation computer networks. As the demand for real-time data processing and low-latency communication grows, particularly with the proliferation of IoT devices, the need for intelligent solutions at the edge of networks becomes increasingly critical.

The key areas of focus for this Special Issue include, but are not limited to, the following:

  • Adaptive Resource Management: Investigating soft computing techniques that enable dynamic resource allocation and load balancing in edge computing environments, ensuring optimal performance and energy efficiency.
  • Intelligent Data Processing: Exploring machine learning algorithms for real-time data analytics at the edge of networks, allowing for immediate insights and decision-making in applications such as smart cities, healthcare, and autonomous systems.
  • Security and Privacy Solutions: Developing innovative soft computing-based security models to protect data integrity and privacy in edge computing scenarios, addressing the challenges posed by distributed data processing.
  • Quality of Service (QoS) Enhancement: Proposing methods that utilize machine learning for predicting and improving the QoS in edge computing applications, ensuring reliable service delivery despite variable network conditions.
  • Collaborative Edge Networks: Studying the application of artificial intelligence to enhance collaboration among edge devices, facilitating efficient communication and data sharing across network nodes.
  • Resilient Edge Architectures: Examining the role of soft computing in designing resilient edge computing architectures that can withstand network disruptions and ensure continuous operation.

By providing a platform for research that integrates soft computing and machine learning with edge computing technologies, this Special Issue aims to attract high-quality submissions from both academia and industry. The innovative approaches that will be discussed in this Special Issue have the potential to significantly advance the field of computer networks, addressing current challenges while paving the way for future developments in intelligent edge computing applications.

Dr. Giovanni Pau
Dr. Fabio Arena
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

  • intelligent edge computing
  • soft computing
  • machine learning
  • IoT
  • Quality of Service (QoS)
  • edge computing

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Published Papers (1 paper)

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Review

40 pages, 2568 KB  
Review
Intelligent Edge Computing and Machine Learning: A Survey of Optimization and Applications
by Sebastián A. Cajas Ordóñez, Jaydeep Samanta, Andrés L. Suárez-Cetrulo and Ricardo Simón Carbajo
Future Internet 2025, 17(9), 417; https://doi.org/10.3390/fi17090417 - 11 Sep 2025
Viewed by 993
Abstract
Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices within distributed network environments, including computational limitations, memory constraints, [...] Read more.
Intelligent edge machine learning has emerged as a paradigm for deploying smart applications across resource-constrained devices in next-generation network infrastructures. This survey addresses the critical challenges of implementing machine learning models on edge devices within distributed network environments, including computational limitations, memory constraints, and energy-efficiency requirements for real-time intelligent inference. We provide comprehensive analysis of soft computing optimization strategies essential for intelligent edge deployment, systematically examining model compression techniques including pruning, quantization methods, knowledge distillation, and low-rank decomposition approaches. The survey explores intelligent MLOps frameworks tailored for network edge environments, addressing continuous model adaptation, monitoring under data drift, and federated learning for distributed intelligence while preserving privacy in next-generation networks. Our work covers practical applications across intelligent smart agriculture, energy management, healthcare, and industrial monitoring within network infrastructures, highlighting domain-specific challenges and emerging solutions. We analyze specialized hardware architectures, cloud offloading strategies, and distributed learning approaches that enable intelligent edge computing in heterogeneous network environments. The survey identifies critical research gaps in multimodal model deployment, streaming learning under concept drift, and integration of soft computing techniques with intelligent edge orchestration frameworks for network applications. These gaps directly manifest as open challenges in balancing computational efficiency with model robustness due to limited multimodal optimization techniques, developing sustainable intelligent edge AI systems arising from inadequate streaming learning adaptation, and creating adaptive network applications for dynamic environments resulting from insufficient soft computing integration. This comprehensive roadmap synthesizes current intelligent edge machine learning solutions with emerging soft computing approaches, providing researchers and practitioners with insights for developing next-generation intelligent edge computing systems that leverage machine learning capabilities in distributed network infrastructures. Full article
(This article belongs to the Special Issue Intelligent Edge Computing: Leveraging Soft Computing and Machine Learning for Next-Generation Network Applications)
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