One of the most promising application areas of the Industrial Internet of Things (IIoT) is Vehicular Ad hoc NETworks (VANETs). VANETs are largely used by Intelligent Transportation Systems (ITS) to provide smart and safe road transport. To reduce the network burden, Software Defined Networks (SDNs) acts as a remote controller. Motivated by the need for greener IIoT solutions, this paper proposes an energy-efficient end-to-end security solution for Software Defined Vehicular Networks (SDVN). Besides SDN's flexible network management, network performance, and energy-efficient end-toend security scheme plays a significant role in providing green IIoT services. Thus, the proposed SDVN provides lightweight end-to-end security. The end-to-end security objective is handled in two levels: i) In RSU-based Group Authentication (RGA) scheme, each vehicle in the RSU range receives a group id-key pair for secure communication and ii) In private-Collaborative Intrusion Detection System (p-CIDS), SDVN detects the potential intrusions inside the VANET architecture using collaborative learning that guarantees privacy through a fusion of differential privacy and homomorphic encryption schemes. The SDVN is simulated in NS2 & MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks. In addition, the p-CIDS detects the intruder with an accuracy of 96.81% in the SDVN.
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 DMLbased IDS detects the malicious IoV with an accuracy of 89.82% compared to traditional ML algorithms.
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