The recent development and adoption of unmanned aerial vehicles (UAVs) is due to its wide variety of applications in public and private sector from parcel delivery to wildlife conservation. The integration of UAVs, 5G, and satellite technologies has prompted telecommunication networks to evolve to provide higher-quality and more stable service to remote areas. However, security concerns with UAVs are growing as UAV nodes are becoming attractive targets for cyberattacks due to enormously growing volumes and poor and weak inbuilt security. In this paper, we propose a UAV- and satellite-based 5G-network security model that can harness machine learning to effectively detect of vulnerabilities and cyberattacks. The solution is divided into two main parts: the model creation for intrusion detection using various machine learning (ML) algorithms and the implementation of ML-based model into terrestrial or satellite gateways. The system identifies various attack types using realistic CSE-CIC IDS-2018 network datasets published by Canadian Establishment for Cybersecurity (CIC). It consists of seven different types of new and contemporary attack types. This paper demonstrates that ML algorithms can be used to classify benign or malicious packets in UAV networks to enhance security. Finally, the tested ML algorithms are compared for effectiveness in terms of accuracy rate, precision, recall, F1-score, and false-negative rate. The decision tree algorithm performed well by obtaining a maximum accuracy rate of 99.99% and a minimum false negative rate of 0% in detecting various attacks as compared to all other types of ML classifiers.

Unmanned aerial vehicles (UAVs) and UAV traffic management (UTM) have drawn attention for applications such as parcel delivery, aerial mapping, agriculture, and surveillance based on line-of-sight (LoS) links. UTM is essential to operate multiple fully autonomous UAVs safely beyond the visual line of sight (BVLoS) in the future dense UAV traffic environment. Various research and development teams globally take UTM initiatives and work on platform testing with different industrial partners. In the future, urban airspace will be congested with various types of autonomous aerial vehicles, thereby resulting in complex air-traffic management caused by communication issues. The UTM requires an efficient communication backbone to handle all airborne communication services. Existing cellular networks are suitable only for terrestrial communication and have limitations in supporting aerial communications. These issues motivate the investigation of an appropriate communication technology for advanced UTM systems. Thus, in this study, we present a future perspective of 6G-enabled UTM ecosystems in a very dense and urban air-traffic scenario focusing on non-terrestrial features, including aerial and satellite communication. We also introduce several urban airspace segmentation and discuss a strategic management framework for dynamic airspace traffic management and conflict-free UAV operations. The UTM enhances the adaptive use of the airspace by shaping the airspace with the overall aim of maximizing the capability and efficiency of the network. We also discuss the 6G multi-layer parameters i.e., space, air, and terrestrial, for safe and efficient urban air transportation in three-dimensional space. Moreover, we discuss the issues and challenges faced by future UTM systems and provide tentative solutions. We subsequently extend the vision of the UTM system and design an advanced and fully autonomous 6G-based UTM system.

With the rapid evolution in wireless communications and autonomous vehicles, intelligent and autonomous vehicles will be launched soon. Vehicle to Everything (V2X) communications provides driving safety, traffic efficiency, and road information in real-time in vehicular networks. V2X has evolved by integrating cellular 5G and New Radio (NR) access technology in V2X communications (i.e., 5G NR V2X); it can fulfill the ever-evolving vehicular application, communication, and service demands of connected vehicles, such as ultra-low latency, ultra-high bandwidth, ultra-high reliability, and security. However, with the increasing number of intelligent and autonomous vehicles and their safety requirements, there is a backlash in deployment and management because of scalability, poor security and less flexibility. Multi-access Edge Computing (MEC) plays a significant role in bringing cloud services closer to vehicular nodes, which reduces the scalability and flexibility issues. In addition, blockchain has evolved as an effective technology enabler to solve several security, privacy, and networking issues faced by the current 5G-based MEC systems in vehicular networks. Blockchain can be integrated as a strong security mechanism for securing and managing 5G V2X along with MEC. In this survey, we discuss, in detail, state-of-the-art V2X, its evolution based on cellular 5G technology and non-cellular 802.11bd. We investigate the integration of blockchain in 5G-based MEC vehicular networks for security, privacy protection, and content caching. We present the issues and challenges in existing edge computing and 5G V2X and, then, we shed some light on future research directions in these integrated and emerging technologies.

Connected and automated vehicles (CAVs) require multiple tasks in their seamless maneuverings. Some essential tasks that require simultaneous management and actions are motion planning, traffic prediction, traffic intersection management, etc. A few of them are complex in nature. Multi-agent reinforcement learning (MARL) can solve complex problems involving simultaneous controls. Recently, many researchers applied MARL in such applications. However, there is a lack of extensive surveys on the ongoing research to identify the current problems, proposed methods, and future research directions in MARL for CAVs. This paper provides a comprehensive survey on MARL for CAVs. A classification-based paper analysis is performed to identify the current developments and highlight the various existing research directions. Finally, the challenges in current works are discussed, and some potential areas are given for exploration to overcome those challenges. Future readers will benefit from this survey and can apply the ideas and findings in their research to solve complex problems.

Providing ubiquitous connectivity for people and equipment with a wide range of service conditions is regarded as the major design goal of 6G networks. Moreover, the future 6G networks need to be extremely flexible to enable reliable and low-latency access for the dynamic number of mobile user equipment. The current base stations (BSs) and remote relay antennas are mostly, on the other hand, installed at fixed geographic locations based on long-term traffic patterns with little re-deployment flexibility. For most 6G applications, where dynamic data traffic occurs in both spatial and temporal domains, such rigid radio access networks (RANs) are unable to maintain ubiquitous connectivity. On the other hand, the rapid advancement of unmanned aerial vehicle (UAV) technology is creating a unique opportunity for the cellular operator to realize flying wireless infrastructure called wireless infrastructure UAV (WI UAV). WI UAV supports wireless connectivity and can stir itself to improve spectral efficiency, coverage, and quality of service of the end-users. This paper introduces 6G new radio in the unlicensed band (NR-U)-based WI UAV that can be used as a base station, relay, or data collector/disseminator. UAV are categorized based on their characteristics, applications, and operations. Further, this paper discusses various regulatory and standardization initiatives for integrating UAVs into the cellular network. Non-standalone NR-U network architecture is designed and explained for WI UAV. Several opportunities and design challenges of NR-U for the WI UAV are discussed and future scopes of WI UAVs are presented.