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.

Intelligent Transportation System (ITS) are helping to enhance road safety and traffic management applications. Internet of Vehicles (IoV) plays a promising role in this field, which turns each vehicle into a smart object with its own compute, storage, and networking capabilities. Nowadays, accidents have been increased mainly due to un-notified alerts about other accidents, work-in-progress, and excessive motorized vehicles at peak times. This non-line of sight information can be efficiently delivered using vehicular communication. IoV network, however has its own challenges like high mobility and dynamic network topology. The above mentioned challenges are addressed with the assistance of a centralized Software Defined Network (SDN), which isolates the control plane from the data plane. In IoV, SDN provides logically centralized traffic management and improves the vehicular communication. In this paper, the Software Defined-Internet of Vehicles (SD-IoV) system is designed to manage heavy traffic and avoids broadcast storm problem with high packet delivery ratio. The proposed broadcast routing mechanism uses selective forwarding and neighbor awareness of the vehicle to efficiently broadcast emergency alert messages, thereby avoiding traffic jams and reducing travel time. On-Board Unit (OBU) in vehicles detects the accident and initializes the broadcast algorithm in SD-IoV system. The accident detection by OBU in vehicles is simulated using machine learning technique with an accuracy of 90%. Simulation performed in SUMO and OMNeT++ shows that with the help of the SDN controller, the IoV network achieves a high packet delivery ratio with minimal delay.

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There are many routing strategies for message delivery in Delay Tolerant Networks. Among them Multicopying routing strategies have been considered the most applicable DTNs. Epidemic routing and two-hop forwarding routing are the two best approaches of DTN. These two approaches belong to multicopying routing strategies as they allow multiple message replicas to increase message delivery delay. This is an advantage, but it might cause expense of additional buffer space and bandwidth overhead. Hence the message scheduling strategy should determine which messages should be dropped in case if buffer is full. Thus, this work deals with a new message scheduling framework for epidemic and two hop forwarding routing in DTNs, such that the decision for forwarding/dropping can be made at a node during each contact for either optimal message delivery ratio or message delivery delay. Extensive simulation results show that this message scheduling framework can achieve better performance.