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 to provide smart and safe road transport. To reduce the network burden, software-defined networks (SDNs) act as a remote controller. Motivated by the need for greener IIoT solutions, this project proposes an energy-efficient end-to-end security solution for software-defined vehicular networks (SDVNs). Besides, SDN’s flexible network management, network performance, and energy-efficient end-to-end 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: 1) in roadside unit (RSU)-based group authentication scheme, each vehicle in the RSU range receives a group ID–key pair for secure communication; and 2) in private collaborative intrusion detection system (p-CIDS), the 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 and MATLAB, and results show increased energy efficiency with lower communication and storage overhead than existing frameworks.


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