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Future Internet
Special Issues
AI-Empowered Future Networks
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AI-Empowered Future Networks

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

Deadline for manuscript submissions: closed (15 May 2022) | Viewed by 8153

Special Issue Editors

Dr. Kandaraj Piamrat

E-Mail Website
Guest Editor
University of Nantes, Nantes, France
Interests: wireless networks, mobile networks, software defined networks; multimedia transmission; quality of experience; knowledge defined networks
Dr. Kamal Singh

E-Mail Website
Guest Editor
University of Saint Etienne, France
Interests: mobile networks; Internet of Things; edge computing; NFV/SDNQoE
Special Issues, Collections and Topics in MDPI journals
Dr. Yassine Hadjadj-Aoul

E-Mail Website
Guest Editor
ESIR, University of Rennes 1, 35042 Rennes CEDEX, France
Interests: quality of service and quality of experience; wireless and mobile networks; future networks; performance evaluation
Special Issues, Collections and Topics in MDPI journals
Dr. Guillaume Muller

E-Mail Website
Guest Editor
University Jean Monnet, St-Etienne, France
Interests: Deep Learning; Federated Learning; Privacy; Few -Shot Learning; VAE; Health-care

Special Issue Information

Dear Colleagues,

In the past few years, management of wireless communications has evolved from using theoretical approaches using mathematical models to practical approaches using machine learning techniques. The emergence of new applications as well as the huge amount of traffic makes it difficult to manage radio resources efficiently, especially in today's heterogeneous and dynamic environment. To handle these issues, researchers in networking community have begun exploring the power of machine learning and artificial intelligence to understand network conditions and usage as well as to extract knowledge from network data. Moreover, with the rise of new machine learning techniques (e.g., federated learning), new possibilities of cooperation between competitive entities (i.e., operators or clients) have appeared, enabling new applications or improvement of global network management.

The aim of this special issue is to invite submissions for new works on applying machine learning techniques in wireless networks. We would like to gather interested researchers from both academia and from industry to propose new ideas, recent results, and future challenges. Potential topics include, but are not limited to:

  • AI-empowered channel allocation
  • AI-empowered handover/access selection
  • AI-empowered radio management (access, sharing, sensing)
  • AI-empowered QoE prediction
  • AI-empowered wireless multimedia networking
  • AI-empowered mobile core networks slicing
  • AI-empowered resource sharing, isolation, and federation
  • AI-empowered strategies for energy-efficient communications
  • AI-empowered strategies for wireless distributed caching systems
  • AI-empowered congestion control for M2M and mMTC communications
  • AI-empowered routing
  • AI-empowered multi-operator network management
  • AI-empowered privacy management

Dr. Kandaraj Piamrat
Dr. Kamal Singh
Dr. Yassine Hadjadj-Aoul
Dr. Guillaume Muller
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

  • Wireless Network
  • Mobile Network
  • Machine Learning
  • Federated Learning
  • Artificial Intelligence
  • Privacy
  • Resource Management
  • Knowledge Defined Network

Published Papers (2 papers)

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Research

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25 pages, 3216 KiB  
Article
TalkRoBots: A Middleware for Robotic Systems in Industry 4.0
by Marwane Ayaida, Nadhir Messai, Frederic Valentin and Dimitri Marcheras
Future Internet 2022, 14(4), 109; https://doi.org/10.3390/fi14040109 - 29 Mar 2022
Cited by 3 | Viewed by 2736
Abstract
This paper proposes a middleware called TalkRoBots that handles interoperability issues, which could be encountered in Industry 4.0. The latter proposes a unified communication approach facilitating the collaboration between heterogeneous equipment without needing to change neither the already used software nor the existing [...] Read more.
This paper proposes a middleware called TalkRoBots that handles interoperability issues, which could be encountered in Industry 4.0. The latter proposes a unified communication approach facilitating the collaboration between heterogeneous equipment without needing to change neither the already used software nor the existing hardware. It allows heterogeneous robots, using both open and proprietary robotic frameworks (i.e., ROS, ABB, Universal Robots, etc.), to communicate and to share information in a transparent manner. It allows robots and Industrial Internet of Things (IIoT) devices to communicate together. Furthermore, a resilience mechanism based on an Artificial Intelligence (AI) approach was designed in order to allow automatically replacing a defective robot with an optimal alternatively available robot. Finally, a remote interface, which could be run through the Cloud, allows users to manipulate fleets of robots from anywhere and to obtain access to sensors’ data. A practical scenario using five different robots has been realized to demonstrate the different possibilities. This demonstrates the cost effectiveness of our middleware in terms of its impacts on the communication network. Finally, a simulation study that evaluates the scalability of our middleware clearly shows that TalkRoBots can be used efficiently in industrial scenarios involving a huge number of heterogeneous robots and IIoT devices. Full article
(This article belongs to the Special Issue AI-Empowered Future Networks)
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Review

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35 pages, 1150 KiB  
Review
Intelligent Traffic Management in Next-Generation Networks
by Ons Aouedi, Kandaraj Piamrat and Benoît Parrein
Future Internet 2022, 14(2), 44; https://doi.org/10.3390/fi14020044 - 28 Jan 2022
Cited by 18 | Viewed by 4581
Abstract
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, [...] Read more.
The recent development of smart devices has lead to an explosion in data generation and heterogeneity. Hence, current networks should evolve to become more intelligent, efficient, and most importantly, scalable in order to deal with the evolution of network traffic. In recent years, network softwarization has drawn significant attention from both industry and academia, as it is essential for the flexible control of networks. At the same time, machine learning (ML) and especially deep learning (DL) methods have also been deployed to solve complex problems without explicit programming. These methods can model and learn network traffic behavior using training data/environments. The research community has advocated the application of ML/DL in softwarized environments for network traffic management, including traffic classification, prediction, and anomaly detection. In this paper, we survey the state of the art on these topics. We start by presenting a comprehensive background beginning from conventional ML algorithms and DL and follow this with a focus on different dimensionality reduction techniques. Afterward, we present the study of ML/DL applications in sofwarized environments. Finally, we highlight the issues and challenges that should be considered. Full article
(This article belongs to the Special Issue AI-Empowered Future Networks)
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