The use of unmanned aerial vehicles (UAVs) is attracting considerable interest in academic research, commercial, and military applications. Multi-UAV systems organized in an ad hoc fashion called a flying ad hoc network (FANET) can cooperatively and collaboratively accomplish complex missions more efficiently compared to single large UAV systems. However, the unique features of FANETs such as high mobility, low node density, and high frequency of topology changes introduce challenges to the communication design, especially routing. Thus, the routing requirements of FANETs surpass those of MANETs or VANETs. In this paper, we present UAV classification, communication and application architectures, and an exhaustive survey of the existing routing protocols for flying ad hoc networks. Furthermore, we highlight the key features, strengths and weaknesses, and different mobility models used for the performance evaluation of the existing FANET routing protocols. More importantly, a proposed taxonomy and a review on the existing FANET routing protocols are presented. Finally, we highlight the existing challenges and open research issues.

Current access infrastructures are characterized by heterogeneity, low latency, high throughput, and high computational capability, enabling massive concurrent connections and various services. Unfortunately, this design does not pay significant attention to mobile services in underserved areas. In this context, the use of aerial radio access networks (ARANs) is a promising strategy to complement existing terrestrial communication systems. Involving airborne components such as unmanned aerial vehicles, drones, and satellites, ARANs can quickly establish a flexible access infrastructure on demand. ARANs are expected to support the development of seamless mobile communication systems toward a comprehensive sixth-generation (6G) global access infrastructure. This paper provides an overview of recent studies regarding ARANs in the literature. First, we investigate related work to identify areas for further exploration in terms of recent knowledge advancements and analyses. Second, we define the scope and methodology of this study. Then, we describe ARAN architecture and its fundamental features for the development of 6G networks. In particular, we analyze the system model from several perspectives, including transmission propagation, energy consumption, communication latency, and network mobility. Furthermore, we introduce technologies that enable the success of ARAN implementations in terms of energy replenishment, operational management, and data delivery. Subsequently, we discuss application scenarios envisioned for these technologies. Finally, we highlight ongoing research efforts and trends toward 6G ARANs.

Vehicular cloud computing (VCC) is an improvement from conventional cloud computing to new revolutionized computing services including intelligent transportation, autonomous driving and vehicle control, Internet browsing, online documentation, and infotainment. It enables vehicles to autonomously share heterogeneous computational resources while solving unanticipated critical problems dynamically. However, in a world of black hats, technology often has a dark side as well. Therefore, the VCC is limited by considerable security and privacy challenges. The special characteristics of VCC, including the multitenancy nature of clouds, intermittent wireless communications, high mobility of vehicles, and rapid resource elasticity with decentralized operations, have promoted security solutions used in vehicular ad hoc networks and conventional cloud computing to be revised. This paper first presents a state-of-the-art study of VCC that focuses on VCC architecture, its features analysis, and extensive VCC applications. Second, the proposed threats identification taxonomy and an exhaustive survey on security and privacy issues in VCC are presented under a layered approach: physical resource layer, vehicle-to-anything (V2X) network layer, and vehicular cloud layer, as well as at a complete system level. Finally, we highlight and discuss challenges and open research issues that can be considered as future research directions.

Given the notable surge in Internet of Things (IoT) devices, low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) have emerged as promising networking components to supplement the network capacity and ensure seamless coverage in 6G, especially over remote areas. However, task offloading and resource management are challenging to realize because of the limited connectivity duration of LEO satellites attributable to their high mobility and UAVs limited resources. Thus, this paper proposes a network model in which mobile edge computing (MEC)-enabled multiple LEO satellites in-orbit provide computational services for a resource-constrained energy harvesting UAV (EH-UAV). The EH-UAV collects data from remote IoT/sensor devices and periodically generates a computational task. To optimize the system model, we formulate a joint LEO-MEC server selection, transmission power allocation, and partial task offloading decision-making problem to maximize the service satisfaction and alleviate energy dissipation under the constraints of connectivity duration, task deadline, and available energy. To circumvent the non-convexity and dynamicity of the problem, it is reformulated as a reinforcement learning problem and solved using a novel mixed discrete-continuous control deep reinforcement learning ( $MDC^{2}-DRL$ ) based algorithm with an action shaping function. Simulation results demonstrate that $MDC^{2}-DRL$ effectively converges and outperforms the existing methods.

Resource management, security, and privacy stand as fundamental pillars for the reliable and secure operation of efficient semantic communications (SC) system. By addressing these aspects, SC system can pave the way for efficient resource utilization, improved network efficiency, enhanced communication performance, and protection of sensitive information. In this study, we begin by presenting the background of SC and reviewing several existing studies in this field. Subsequently, we provide a comprehensive and exhaustive survey of resource management, security, and privacy in SC. We identify and highlight existing challenges and open research challenges related to resource management, security, and privacy in SC in order to spur further investigation in these areas.

The integration of unmanned aerial vehicles (UAVs) into future-generation wireless networks is a promising strategy for complementing terrestrial communication infrastructures that will lead to capacity and coverage improvements. Unfortunately, the energy limitations of the existing off-the-shelf small-size UAVs are significant challenges, especially during long missions. In this article, first, we investigate the communication model of a heterogeneous UAV-assisted wireless network that consists of multiple energy-constrained battery-powered small-size UAVs (a.k.a. follower UAVs) and a large-size UAV (a.k.a. dispatcher UAV) with solar energy harvesting capability. Subsequently, we propose an aerial energy sharing algorithm to improve the lifetime of the system by orchestrating energy balancing among follower UAVs. In particular, the dispatcher UAV having large energy capacity owing to its battery size and solar energy harvesting capability shares its energy with follower UAVs using radio-frequency-based power transfer through beamforming. The proposed energy sharing algorithm considers the placement, available battery capacity, and mission execution time of the follower UAVs to decide the energy sharing points with optimum efficiency. Extensive simulations show that our proposed algorithm outperforms existing algorithms in terms of charging efficiency and energy balancing among UAVs.