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SDN-Enabled Sensing in Smart Infrastructure

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

Deadline for manuscript submissions: 1 November 2024 | Viewed by 5965

Special Issue Editors


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Guest Editor
Department of Engineering and Mathematics, Sheffield Hallam University, Howard Street, Sheffield S1 1WB, UK
Interests: smart grid technologies; IoT systems; SDN/NFV; power line communication; energy management; intelligent systems for critical infrastructure; smart wireless power transfer; transactive energy systems and resource allocation & control in communication systems
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Computing, Sheffield Hallam University, Sheffield, UK
Interests: context sensing, context-aware intelligent systems, IoT, data analytics, mobile computing
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Engineering, Computing and Design, University of Chichester, Chichester PO19 6PE, UK
Interests: intelligent reflecting surfaces (IRS); smart signal processing; massive MIMO; 5G and beyond; machine learning; optimization; Internet of Things (IoT); smart energy cities
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development in sensor technologies, computing and analytics has enabled the creation of new techniques used to monitor physical infrastructure. However, many of the underlying sensor/data networks in critical infrastructure are unable to self-reconfigure or fulfil the requirements of various applications. Furthermore, the constrained resources in edge/fog nodes limit their ability to process large quantities of data to accurately determine the optimum decision in real time. This makes remote processing unable to minimise the conflict between accuracy and timeliness of decisions. SDN allows the network to programmatically respond to the changes in the communication environment, traffic requirements and operating context.   

In critical infrastructure such as transportation systems, agriculture, smart grids, smart healthcare systems, robotic systems, etc., a loss of information of delays can lead to a loss of human lives or damage to expensive equipment, even if the actions are accurate. The agility and centralised management of data traffic provided by SDN opens new possibilities in the management of critical infrastructure.    

This Special Issue (SI) aims to bring together researchers and practitioners who are interested in the development of these techniques to understand or address the issues related to the application of SDN in critical infrastructure architectures, algorithms, and applications.

The list of possible topics includes, but is not limited to, the following:

  • Novel theories, concepts, and paradigm edge/IoT convergence;
  • Cybersecurity and mitigation in SDN;
  • Application of machine learning in SDN;
  • Surveys and tutorials;
  • Resource allocation and sharing;
  • Congestion management;
  • Quality of service;
  • Novel architectures;
  • Context awareness;
  • Non-deterministic communication;
  • Network automation;
  • Integration of blockchain;
  • SDN-based services in edge computing;
  • SDN for real-time industrial IoT applications;
  • Future directions;
  • Novel collaborative frameworks/algorithms/protocols for intelligent IoT applications;
  • SDN-enabled reliability and low latency for industrial IoT applications;
  • Applications of SDN in critical infrastructure.

Dr. Augustine Ikpehai
Dr. Abayomi Otebolaku
Dr. Kelvin Anoh
Dr. Bernardi Pranggono
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. 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

  • industrial Internet of Things
  • software-defined network
  • edge processing
  • smart infrastructure systems
  • machine learning
  • centralised management
  • artificial intelligence
  • cognitive communication and networking
  • intrusion detection
  • privacy and security

Published Papers (1 paper)

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48 pages, 10839 KiB  
Systematic Review
A Systematic Literature Review on Machine Learning and Deep Learning Approaches for Detecting DDoS Attacks in Software-Defined Networking
by Abdullah Ahmed Bahashwan, Mohammed Anbar, Selvakumar Manickam, Taief Alaa Al-Amiedy, Mohammad Adnan Aladaileh and Iznan H. Hasbullah
Sensors 2023, 23(9), 4441; https://doi.org/10.3390/s23094441 - 1 May 2023
Cited by 15 | Viewed by 5481
Abstract
Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. [...] Read more.
Software-defined networking (SDN) is a revolutionary innovation in network technology with many desirable features, including flexibility and manageability. Despite those advantages, SDN is vulnerable to distributed denial of service (DDoS), which constitutes a significant threat due to its impact on the SDN network. Despite many security approaches to detect DDoS attacks, it remains an open research challenge. Therefore, this study presents a systematic literature review (SLR) to systematically investigate and critically analyze the existing DDoS attack approaches based on machine learning (ML), deep learning (DL), or hybrid approaches published between 2014 and 2022. We followed a predefined SLR protocol in two stages on eight online databases to comprehensively cover relevant studies. The two stages involve automatic and manual searching, resulting in 70 studies being identified as definitive primary studies. The trend indicates that the number of studies on SDN DDoS attacks has increased dramatically in the last few years. The analysis showed that the existing detection approaches primarily utilize ensemble, hybrid, and single ML-DL. Private synthetic datasets, followed by unrealistic datasets, are the most frequently used to evaluate those approaches. In addition, the review argues that the limited literature studies demand additional focus on resolving the remaining challenges and open issues stated in this SLR. Full article
(This article belongs to the Special Issue SDN-Enabled Sensing in Smart Infrastructure)
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