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C-V2X aims at resolving issues pertaining to the traditional usability of V2I and V2V networking. Specifically, C-V2X lowers the number of entities involved in vehicular communications and allows the inclusion of cellular-security solutions to be applied to V2X. For this, the evolvement of LTE-V2X is revolutionary, but it fails to handle the demands of high throughput, ultrahigh reliability, and ultra-low latency alongside its security mechanisms. To counter this, 5G-V2X is considered as an integral solution, which not only resolves the issues related to LTE-V2X but also provides a function-based network setup. Several reports have been given for the security of 5G, but none of them primarily focuses on the security of 5G-V2X and its supportive mobile backhaul. This article provides a detailed overview of 5G-V2X with a security-based comparison to LTE-V2X. A novel Security Reflex Function (SRF)-based architecture is proposed and several research challenges are presented related to the security of 5G-V2X. Furthermore, the article lays out requirements of Ultra-Dense and Ultra-Secure (UD-US) transmissions necessary for 5G-V2X. [ABSTRACT FROM AUTHOR]

Compared to traditional networking, SDN has better controllability and visibility for network components, which enable better management by using the common controller. In this article, the standard architecture of SDN is enhanced to utilize UAVs as on-demand forwarding switches. The proposed approach can achieve efficient management and fast handovers by decreasing the handover latency, E2E delay, and signaling overheads. The illustrated scenarios will help in understanding the impact of existing handover approaches in the next generation wireless networks, especially the upcoming 5G, which includes small cells, UAVs, UEs, and so on. The simulation study shows that scenarios with both UAVs and small cells perform better than scenarios with only small cells. The results in this article show that the proposed SDNbased handover scenarios perform better than the existing 4G-LTE handover for UAVs. [ABSTRACT FROM AUTHOR]

The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite. [ABSTRACT FROM AUTHOR]