Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV

The massive increase in computing and network capabilities has resulted in a paradigm shift from vehicular networks to the Internet of Vehicles (IoV). Owing to the dynamic and heterogeneous nature of IoV, it requires efficient resource management using smart technologies such as Software Defined Net...

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Main Authors: Anbalagan, S, Bashir, AK, Raja, G, Dhanasekaran, P, Vijayaraghavan, G, Tariq, U, Guizani, M
Format: Article in Journal/Newspaper
Language:English
Published: Institute of Electrical and Electronics Engineers 2021
Subjects:
DML
Online Access:https://e-space.mmu.ac.uk/627615/1/IEEE%20IoT-%20Gunasekaran.pdf
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spelling ftmanchuniv:oai:e-space.mmu.ac.uk:627615 2024-05-19T07:39:28+00:00 Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV Anbalagan, S Bashir, AK Raja, G Dhanasekaran, P Vijayaraghavan, G Tariq, U Guizani, M 2021-09-15 text https://e-space.mmu.ac.uk/627615/1/IEEE%20IoT-%20Gunasekaran.pdf en eng Institute of Electrical and Electronics Engineers https://e-space.mmu.ac.uk/627615/ https://ieeexplore.ieee.org/document/9390178 10.1109/JIOT.2021.3069642 https://e-space.mmu.ac.uk/627615/1/IEEE%20IoT-%20Gunasekaran.pdf Anbalagan, S </view/creators/Anbalagan=3AS=3A=3A.html>, Bashir, AK </view/creators/Bashir=3AAK=3A=3A.html>, Raja, G </view/creators/Raja=3AG=3A=3A.html>, Dhanasekaran, P </view/creators/Dhanasekaran=3AP=3A=3A.html>, Vijayaraghavan, G </view/creators/Vijayaraghavan=3AG=3A=3A.html>, Tariq, U </view/creators/Tariq=3AU=3A=3A.html> and Guizani, M </view/creators/Guizani=3AM=3A=3A.html> (2021) Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV. IEEE Internet of Things Journal, 8 (18). pp. 13950-13957. ISSN 2327-4662 info:eu-repo/semantics/openAccess Article PeerReviewed 2021 ftmanchuniv 2024-04-30T23:59:52Z The massive increase in computing and network capabilities has resulted in a paradigm shift from vehicular networks to the Internet of Vehicles (IoV). Owing to the dynamic and heterogeneous nature of IoV, it requires efficient resource management using smart technologies such as Software Defined Network (SDN), Machine Learning (ML), and so on. Road Side Units (RSUs) in Software Defined-IoV (SD-IoV) networks are responsible for network efficiency and offer several safety functions. However, it is not viable to deploy enough RSUs, and also the existing RSU placement lacks universal coverage within a region. Further, any disruption in network performance or security impacts vehicular activities severely. Thus, this work aims to improve network efficiency through optimal RSU placement and enhance security with a malicious IoV detection algorithm in an SD-IoV network. Therefore, the Memetic-based RSU (M-RSU) placement algorithm is proposed to reduce communication delay and increase the coverage area among IoV devices through an optimum RSU deployment. Besides the M-RSU algorithm, the work also proposes a Distributed ML (DML)-based Intrusion Detection System (IDS) that prevents the SD-IoV network from disastrous security failures. The simulation results show that M-RSU placement reduces the transmission delay. The DML-based IDS detects the malicious IoV with an accuracy of 89.82% compared to traditional ML algorithms. Article in Journal/Newspaper DML eSpace - Manchester Metropolitan University's Research Repository
institution Open Polar
collection eSpace - Manchester Metropolitan University's Research Repository
op_collection_id ftmanchuniv
language English
description The massive increase in computing and network capabilities has resulted in a paradigm shift from vehicular networks to the Internet of Vehicles (IoV). Owing to the dynamic and heterogeneous nature of IoV, it requires efficient resource management using smart technologies such as Software Defined Network (SDN), Machine Learning (ML), and so on. Road Side Units (RSUs) in Software Defined-IoV (SD-IoV) networks are responsible for network efficiency and offer several safety functions. However, it is not viable to deploy enough RSUs, and also the existing RSU placement lacks universal coverage within a region. Further, any disruption in network performance or security impacts vehicular activities severely. Thus, this work aims to improve network efficiency through optimal RSU placement and enhance security with a malicious IoV detection algorithm in an SD-IoV network. Therefore, the Memetic-based RSU (M-RSU) placement algorithm is proposed to reduce communication delay and increase the coverage area among IoV devices through an optimum RSU deployment. Besides the M-RSU algorithm, the work also proposes a Distributed ML (DML)-based Intrusion Detection System (IDS) that prevents the SD-IoV network from disastrous security failures. The simulation results show that M-RSU placement reduces the transmission delay. The DML-based IDS detects the malicious IoV with an accuracy of 89.82% compared to traditional ML algorithms.
format Article in Journal/Newspaper
author Anbalagan, S
Bashir, AK
Raja, G
Dhanasekaran, P
Vijayaraghavan, G
Tariq, U
Guizani, M
spellingShingle Anbalagan, S
Bashir, AK
Raja, G
Dhanasekaran, P
Vijayaraghavan, G
Tariq, U
Guizani, M
Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
author_facet Anbalagan, S
Bashir, AK
Raja, G
Dhanasekaran, P
Vijayaraghavan, G
Tariq, U
Guizani, M
author_sort Anbalagan, S
title Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
title_short Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
title_full Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
title_fullStr Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
title_full_unstemmed Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV
title_sort machine learning-based efficient and secure rsu placement mechanism for software defined-iov
publisher Institute of Electrical and Electronics Engineers
publishDate 2021
url https://e-space.mmu.ac.uk/627615/1/IEEE%20IoT-%20Gunasekaran.pdf
genre DML
genre_facet DML
op_relation https://e-space.mmu.ac.uk/627615/
https://ieeexplore.ieee.org/document/9390178
10.1109/JIOT.2021.3069642
https://e-space.mmu.ac.uk/627615/1/IEEE%20IoT-%20Gunasekaran.pdf
Anbalagan, S </view/creators/Anbalagan=3AS=3A=3A.html>, Bashir, AK </view/creators/Bashir=3AAK=3A=3A.html>, Raja, G </view/creators/Raja=3AG=3A=3A.html>, Dhanasekaran, P </view/creators/Dhanasekaran=3AP=3A=3A.html>, Vijayaraghavan, G </view/creators/Vijayaraghavan=3AG=3A=3A.html>, Tariq, U </view/creators/Tariq=3AU=3A=3A.html> and Guizani, M </view/creators/Guizani=3AM=3A=3A.html> (2021) Machine Learning-based Efficient and Secure RSU Placement Mechanism for Software Defined-IoV. IEEE Internet of Things Journal, 8 (18). pp. 13950-13957. ISSN 2327-4662
op_rights info:eu-repo/semantics/openAccess
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