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Applied Sciences
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Exploiting Big Data in Communication Networks
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Exploiting Big Data in Communication Networks

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Electrical, Electronics and Communications Engineering".

Deadline for manuscript submissions: closed (15 November 2020) | Viewed by 9981

Special Issue Editors

Prof. Dr. Petros Nicopolitidis

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Guest Editor
Department of Informatics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: wireless networks; optical networks; nanonetworks; network security
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sanjay Kumar Dhurandher

E-Mail Website
Guest Editor
Department of Information Technology, Netaji Subhas University of Technology, New Delhi, India
Interests: wireless networks; underwater sensor networks; opportunistic networks; cognitive radio networks; IoT; network security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of the networking industry and the market penetration of related products has yielded unprecedented volumes of exchanged data, which have been known as Big Data. These large datasets can be exploited to provide intelligence to the underlying network infrastructure, as well as to offered services, so as to increase metrics related indicatively to performance, user experience, offered security and privacy, etc. Nevertheless, efficient exploitation of the vast amount of data calls for new and efficient methods that will collect, analyze, and employ such data to achive the desired network and service functionality.

The research community is currently seeking to capture and analyze Big Data stemming from communication networks to improve operations and management for communication systems by incorporating intelligence. Therefore, the purpose of this Special Issue is to strengthen this effort further, by inviting contributions that address the added value brought by Big Data to communication networks infrastructures and services. Topics of interest include but are not limited to the following areas:

Big data collection in communication networks;

Exploiting adaptive techniques for increased network performance;

Big data exploitation in wireless networks;

Big data exploitation in optical networks;

Big data and network security;

Big data for network service intelligence.

We hope this Special Issue works as a roadmap for all communication network researchers.

Prof. Petros Nicopolitidis
Prof. Sanjay Kumar Dhurandher
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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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 (4 papers)

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Research

16 pages, 2082 KiB  
Article
ARMONIA: A Unified Access and Metro Network Architecture
by Aristotelis Kretsis, Ippokratis Sartzetakis, Polyzois Soumplis, Katerina Mitropoulou, Panagiotis Kokkinos, Petros Nicopolitidis, Georgios Papadimitriou and Emmanouel Varvarigos
Appl. Sci. 2020, 10(23), 8318; https://doi.org/10.3390/app10238318 - 24 Nov 2020
Cited by 3 | Viewed by 1824
Abstract
We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro [...] Read more.
We present a self-configured and unified access and metro network architecture, named ARMONIA. The ARMONIA network monitors its status, and dynamically (re-)optimizes its configuration. ARMONIA leverages software defined networking (SDN) and network functions virtualization (NFV) technologies. These technologies enable the access and metro convergence and the joint and efficient control of the optical and the IP equipment used in these different network segments. Network monitoring information is collected and analyzed utilizing machine learning and big data analytics methods. Dynamic algorithms then decide how to adapt and dynamically optimize the unified network. The ARMONIA network enables unprecedented resource efficiency and provides advanced virtualization services, reducing the capital expenditures (CAPEX) and operating expenses (OPEX) and lowering the barriers for the introduction of new services. We demonstrate the benefits of the ARMONIA network in the context of dynamic resource provisioning of network slices. We observe significant spectrum and equipment savings when compared to static overprovisioning. Full article
(This article belongs to the Special Issue Exploiting Big Data in Communication Networks)
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24 pages, 1394 KiB  
Article
NTARC: A Data Model for the Systematic Review of Network Traffic Analysis Research
by Félix Iglesias, Daniel C. Ferreira, Gernot Vormayr, Maximilian Bachl and Tanja Zseby
Appl. Sci. 2020, 10(12), 4307; https://doi.org/10.3390/app10124307 - 23 Jun 2020
Cited by 3 | Viewed by 3377
Abstract
The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly [...] Read more.
The increased interest in secure and reliable communications has turned the analysis of network traffic data into a predominant topic. A high number of research papers propose methods to classify traffic, detect anomalies, or identify attacks. Although the goals and methodologies are commonly similar, we lack initiatives to categorize the data, methods, and findings systematically. In this paper, we present Network Traffic Analysis Research Curation (NTARC), a data model to store key information about network traffic analysis research. We additionally use NTARC to perform a critical review of the field of research conducted in the last two decades. The collection of descriptive research summaries enables the easy retrieval of relevant information and a better reuse of past studies by the application of quantitative analysis. Among others benefits, it enables the critical review of methodologies, the detection of common flaws, the obtaining of baselines, and the consolidation of best practices. Furthermore, it provides a basis to achieve reproducibility, a key requirement that has long been undervalued in the area of traffic analysis. Thus, besides reading hard copies of papers, with NTARC, researchers can make use of a digital environment that facilitates queries and reviews over a comprehensive field corpus. Full article
(This article belongs to the Special Issue Exploiting Big Data in Communication Networks)
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15 pages, 2433 KiB  
Article
Key Quality Indicators Prediction for Web Browsing with Embedded Filter Feature Selection
by Su Xie, Ke Li, Mingming Xiao, Le Zhang and Wanlin Li
Appl. Sci. 2020, 10(6), 2141; https://doi.org/10.3390/app10062141 - 21 Mar 2020
Cited by 3 | Viewed by 2120
Abstract
In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about [...] Read more.
In this paper, the prediction of over-the-top service quality is discussed, which is a promising way for mobile network engineers to tackle service deterioration as early as possible. Currently, traditional mobile network operation often takes appropriate remedial measures, when receiving customers’ complaints about service problems. With the popularity of over-the-top services, this problem has become increasingly serious. Based on the service perception data crowd-sensed from massive smartphones in the mobile network, we first investigated the application of multi-label ReliefF, a well-known method of feature selection, in determining the feature weights of the perception data and propose a unified multi-label ReliefF (UML-ReliefF) algorithm. Then a feature-weighted multi-label k-nearest neighbor (ML-kNN) algorithm is proposed for the key quality indicators (KQI) prediction, by combining the UML-ReliefF and ML-kNN together in the learning. The experimental results for web browsing service show that UML-ReliefF can effectively identify the most influential features of the data and thus, lead to better performance for KQI prediction. The experiments also show that the feature-weighted KQI prediction is superior to its unweighted counterpart, since the former takes full advantage of all the features in the learning. Although there is still much room of improvement in the precision of the prediction, the proposed method is highly potential for network engineers to find the deterioration of service quality promptly and take measures before it is too late. Full article
(This article belongs to the Special Issue Exploiting Big Data in Communication Networks)
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15 pages, 7037 KiB  
Article
Design of Optical Tunnel Switching Networks for Big Data Applications
by Yuh-Jiuh Cheng, Bor-Tauo Chen, Cheng-Ping Wu and Yu-Yun Lee
Appl. Sci. 2020, 10(6), 2098; https://doi.org/10.3390/app10062098 - 20 Mar 2020
Cited by 4 | Viewed by 2098
Abstract
In this paper, we proposed large-scale optical tunnel switching networks based on the Torus topology network with WSS (Wavelength Selective Switch) for future big data applications. All nodes of the large-scale optical tunnel switching networks use WSS switch modules, and the communications between [...] Read more.
In this paper, we proposed large-scale optical tunnel switching networks based on the Torus topology network with WSS (Wavelength Selective Switch) for future big data applications. All nodes of the large-scale optical tunnel switching networks use WSS switch modules, and the communications between nodes use multiple λs (wavelengths), where a tunnel is established with a wavelength which can be reused. The widely used MEMS (Micro-Electro-Mechanical Systems) and LCoS (Liquid Crystal on Silicon) technologies are all millisecond-level switching speeds, so the frame size of the optical frame switch is very large, and this will reduce switching performance. Therefore, they are only suitable for optical tunnel switching networks design, but are not suitable for optical frame switch design. This multi-plane Torus topology network architecture not only increases network throughput, but also has fault tolerance to increase network reliability. When the traffic is changed, the number of tunnels between nodes can be scheduled in time to balance the load traffic and avoid traffic loss. Therefore, it can not only schedule the number of tunnels in time to balance the load traffic, in order to avoid traffic loss, but also because the channel is fixedly established, this does not generate any buffer delay, and this because of the transmission using optical transmission unlimited speed, so it is a good choice for future big data applications that require high speed, high bandwidth and low latency. Full article
(This article belongs to the Special Issue Exploiting Big Data in Communication Networks)
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