Special Issue "Future Networks: Latest Trends and Developments"

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: closed (30 October 2020).

Special Issue Editors

Prof. Dr. Paolo Bellavista
E-Mail Website
Guest Editor
Department of Computer Science and Engineering (DISI), University of Bologna, 40136 Bologna, Italy
Interests: wireless sensor and actuator networks; middleware for sensor and actuator networks; vehicular sensor networks; edge computing; fog computing; online stream processing of sensing dataflows; IoT and big data processing; pervasive and mobile computing; cooperative networking; cyber physical systems for Industry 4.0
Special Issues and Collections in MDPI journals
Dr. Aaqif Afzaal Abbasi
E-Mail Website
Guest Editor
Department of Software Engineering, Foundation University, Islamabad, Pakistan
Interests: cloud computing; data center performance optimization; edge computing; high-performance computing; internet of things; network resource allocation and management; parallel and distributed systems
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the rapid increase in the use of internet, web-based services, and social media, computer and communication networks have developed at a very fast pace. The aim behind the change was to develop certain technologies to address user requirements and accommodate new users.

In this context, we have witnessed technologies such as grid computing, cloud computing, fog computing, edge computing, and dew computing. The developments kept going, and the concepts of Internet of Things (IoT), smart cities and Software-defined networking (SDN) were introduced. By closely observing these computer networking technologies, we can clearly see that the purpose behind them is to ease network manageability, configuration, and reduction of network resource allocation issues.

The purpose of this Special Issue is to provide a platform for researchers to share their research experiences, both theoretical and practical, in defining the issues, challenges, and proposed solutions to address network resource allocation concerns in future computer and communication networks. We are looking for contributions in the best interest of the network research community. The potential topics of interest include but are not limited to the following:

  • Cloud computing;
  • Edge computing;
  • Fog computing;
  • Dew computing;
  • Smart cities;
  • Internet of Things (IoT);
  • Software-defined networking (SDN);
  • Network function virtualization (NFV);
  • Data centers;
  • Future networks and internet;
  • Network architectures.

Prof. Dr. Paolo Bellavista
Dr. Aaqif Afzaal Abbasi
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 papers will be 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 1400 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.

Published Papers (3 papers)

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Research

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Article
An Improved Deep Belief Network Prediction Model Based on Knowledge Transfer
Future Internet 2020, 12(11), 188; https://doi.org/10.3390/fi12110188 - 29 Oct 2020
Viewed by 776
Abstract
A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic [...] Read more.
A deep belief network (DBN) is a powerful generative model based on unlabeled data. However, it is difficult to quickly determine the best network structure and gradient dispersion in traditional DBN. This paper proposes an improved deep belief network (IDBN): first, the basic DBN structure is pre-trained and the learned weight parameters are fixed; secondly, the learned weight parameters are transferred to the new neuron and hidden layer through the method of knowledge transfer, thereby constructing the optimal network width and depth of DBN; finally, the top-down layer-by-layer partial least squares regression method is used to fine-tune the weight parameters obtained by the pre-training, which avoids the traditional fine-tuning problem based on the back-propagation algorithm. In order to verify the prediction performance of the model, this paper conducts benchmark experiments on the Movielens-20M (ML-20M) and Last.fm-1k (LFM-1k) public data sets. Compared with other traditional algorithms, IDBN is better than other fixed models in terms of prediction performance and training time. The proposed IDBN model has higher prediction accuracy and convergence speed. Full article
(This article belongs to the Special Issue Future Networks: Latest Trends and Developments)
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Article
Performance Model for Video Service in 5G Networks
Future Internet 2020, 12(6), 99; https://doi.org/10.3390/fi12060099 - 08 Jun 2020
Viewed by 1485
Abstract
Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a [...] Read more.
Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach. Full article
(This article belongs to the Special Issue Future Networks: Latest Trends and Developments)
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Review

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Review
Software Defined Networking Flow Table Management of OpenFlow Switches Performance and Security Challenges: A Survey
Future Internet 2020, 12(9), 147; https://doi.org/10.3390/fi12090147 - 31 Aug 2020
Cited by 9 | Viewed by 1488
Abstract
Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and [...] Read more.
Software defined networking (SDN) is an emerging network paradigm that decouples the control plane from the data plane. The data plane is composed of forwarding elements called switches and the control plane is composed of controllers. SDN is gaining popularity from industry and academics due to its advantages such as centralized, flexible, and programmable network management. The increasing number of traffics due to the proliferation of the Internet of Thing (IoT) devices may result in two problems: (1) increased processing load of the controller, and (2) insufficient space in the switches’ flow table to accommodate the flow entries. These problems may cause undesired network behavior and unstable network performance, especially in large-scale networks. Many solutions have been proposed to improve the management of the flow table, reducing controller processing load, and mitigating security threats and vulnerabilities on the controllers and switches. This paper provides comprehensive surveys of existing schemes to ensure SDN meets the quality of service (QoS) demands of various applications and cloud services. Finally, potential future research directions are identified and discussed such as management of flow table using machine learning. Full article
(This article belongs to the Special Issue Future Networks: Latest Trends and Developments)
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