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Advanced Management for Full-Automized Networks in Post-COVID Era
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Advanced Management for Full-Automized Networks in Post-COVID Era

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 7743

Special Issue Editor

Dr. Jordi Mongay Batalla

E-Mail Website
Guest Editor
Institute of Telecommunications, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland
Interests: cybersecurity (risk assessment, security enforcement, vulnerability management); IP technologies (radio: 5G and 6G; core: network service chain, SDN, AI); applications (DLT and blockchain, Internet of Things, smart cities, multimedia) for the Future Internet
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

COVID-19 has introduced a revolution of ICT technologies directed by an increasing demand for new applications and services. This demand has also introduced several challenges to the management of underlying networks and services (e.g., cloud services). During the pandemic, management of networks relied on the hands-on intervention of engineers; however, it has been clear that the network capacity of self-configuration, self-optimization and self-healing should be improved in order to tackle future critical scenarios.

Contributions to this Special Issue should provide new visions to the development of networks liable to suffering from an increased demand of services including very high peak demands. Solutions to the scalability of users, services and digital applications could include new mechanisms for management, security assessment and recovery of all types of networks, from extended mobile networks to small local area networks or sensor networks.

Dr. Jordi Mongay Batalla
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 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. Sensors 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 2600 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

  • Network management techniques
  • AI/ML for network management and/or security
  • Adaptive security mechanisms
  • Profile management and resource control
  • Dimensioning of networks
  • Radio resource control through prioritization
  • Massive connectivity of devices
  • SDN/NFV-based scalable networks
  • Software-based architecture deployment

Published Papers (4 papers)

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Research

25 pages, 6291 KiB  
Article
Implementation of IMS/NGN Transport Stratum Based on the SDN Concept
by Sylwester Kaczmarek, Maciej Sac and Kamil Bachorski
Sensors 2023, 23(12), 5481; https://0-doi-org.brum.beds.ac.uk/10.3390/s23125481 - 10 Jun 2023
Viewed by 1061
Abstract
The paper presents the development and verification of software and a testbed aiming to demonstrate the ability of two telecommunication network concepts—Next Generation Network (NGN) and Software-Defined Networking (SDN)—to cooperate. The proposed architecture includes components of the IP Multimedia Subsystem (IMS) in its [...] Read more.
The paper presents the development and verification of software and a testbed aiming to demonstrate the ability of two telecommunication network concepts—Next Generation Network (NGN) and Software-Defined Networking (SDN)—to cooperate. The proposed architecture includes components of the IP Multimedia Subsystem (IMS) in its service stratum and of the SDN (controller and programmable switches) in its transport stratum, providing flexible transport resource control and management via open interfaces. One important feature of the presented solution is the inclusion of ITU-T standards for NGN networks, which are not considered in other related works. The paper includes details regarding the hardware and software architecture of the proposed solution as well as results of the performed functional tests, which confirm its proper operation. Full article
(This article belongs to the Special Issue Advanced Management for Full-Automized Networks in Post-COVID Era)
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15 pages, 637 KiB  
Article
Support for the Vulnerability Management Process Using Conversion CVSS Base Score 2.0 to 3.x
by Maciej Roman Nowak, Michał Walkowski and Sławomir Sujecki
Sensors 2023, 23(4), 1802; https://0-doi-org.brum.beds.ac.uk/10.3390/s23041802 - 06 Feb 2023
Cited by 2 | Viewed by 1655
Abstract
COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key [...] Read more.
COVID-19 forced a number of changes in many areas of life, which resulted in an increase in human activity in cyberspace. Furthermore, the number of cyberattacks has increased. In such circumstances, detection, accurate prioritisation, and timely removal of critical vulnerabilities is of key importance for ensuring the security of various organisations. One of the most-commonly used vulnerability assessment standards is the Common Vulnerability Scoring System (CVSS), which allows for assessing the degree of vulnerability criticality on a scale from 0 to 10. Unfortunately, not all detected vulnerabilities have defined CVSS base scores, or if they do, they are not always expressed using the latest standard (CVSS 3.x). In this work, we propose using machine learning algorithms to convert the CVSS vector from Version 2.0 to 3.x. We discuss in detail the individual steps of the conversion procedure, starting from data acquisition using vulnerability databases and Natural Language Processing (NLP) algorithms, to the vector mapping process based on the optimisation of ML algorithm parameters, and finally, the application of machine learning to calculate the CVSS 3.x vector components. The calculated example results showed the effectiveness of the proposed method for the conversion of the CVSS 2.0 vector to the CVSS 3.x standard. Full article
(This article belongs to the Special Issue Advanced Management for Full-Automized Networks in Post-COVID Era)
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29 pages, 22099 KiB  
Article
Modeling Real-Life Urban Sensor Networks Based on Open Data
by Bartosz Musznicki, Maciej Piechowiak and Piotr Zwierzykowski
Sensors 2022, 22(23), 9264; https://0-doi-org.brum.beds.ac.uk/10.3390/s22239264 - 28 Nov 2022
Cited by 6 | Viewed by 1972
Abstract
Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular [...] Read more.
Epidemics and pandemics dramatically affect mobility trends around the world, which we have witnessed recently and expect more of in the future. A global energy crisis is looming ahead on the horizon and will redefine the transportation and energy usage patterns, in particular in large cities and metropolitan areas. As the trend continues to expand, the need to efficiently monitor and manage smart city infrastructure, public transportation, service vehicles, and commercial fleets has become of higher importance. This, in turn, requires new methods for dissemination, collection, and processing of data from massive number of already deployed sensing devices. In order to transmit these data efficiently, it is necessary to optimize the connection structure in wireless networks. Emerging open access to real data from different types of networked and sensing devices should be leveraged. It enables construction of models based on frequently updated real data rather than synthetic models or test environments. Hence, the main objective of this article is to introduce the concept of network modeling based on publicly available geographic location data of heterogeneous nodes and to promote the use of real-life diverse open data sources as the basis of novel research related to urban sensor networks. The feasibility of designed modeling architecture is discussed and proved with numerous examples of modeled spatial and spatiotemporal graphs, which are essential in opportunistic routing-related studies using the methods which rely on graph theory. This approach has not been considered before in similar studies and in the literature. Full article
(This article belongs to the Special Issue Advanced Management for Full-Automized Networks in Post-COVID Era)
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18 pages, 562 KiB  
Article
A Method for Cheating Indication in Unproctored On-Line Exams
by Dan Komosny and Saeed Ur Rehman
Sensors 2022, 22(2), 654; https://0-doi-org.brum.beds.ac.uk/10.3390/s22020654 - 15 Jan 2022
Cited by 2 | Viewed by 2145
Abstract
COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In [...] Read more.
COVID-19 has disrupted every field of life and education is not immune to it. Student learning and examinations moved on-line on a few weeks notice, which has created a large workload for academics to grade the assessments and manually detect students’ dishonesty. In this paper, we propose a method to automatically indicate cheating in unproctored on-line exams, when somebody else other than the legitimate student takes the exam. The method is based on the analysis of the student’s on-line traces, which are logged by distance education systems. We work with customized IP geolocation and other data to derive the student’s cheating risk score. We apply the method to approx. 3600 students in 22 courses, where the partial or final on-line exams were unproctored. The found cheating risk scores are presented along with examples of indicated cheatings. The method can be used to select students for knowledge re-validation, or to compare student cheating across courses, age groups, countries, and universities. We compared student cheating risk scores between four academic terms, including two terms of university closure due to COVID-19. Full article
(This article belongs to the Special Issue Advanced Management for Full-Automized Networks in Post-COVID Era)
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