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.

This paper proposes a novel multi-agent deep reinforcement learning based algorithm which can realize federated learning computation with Internet-of-Underwater-Things (IoUT) devices in ocean environment. According to the fact that underwater networks is relatively not easy to setup reliable links by huge fading compared to wireless free-space air medium, gathering all training data for conducting centralized deep learning training is not easy. Therefore, federated learning (FL) based distributed deep learning can be a suitable solution for this application. In this IoUT network (IoUT-Net) scenario, the FL system needs to construct a global learning model by aggregating the local model parameters those are obtained from individual IoUT devices. In order to reliably deliver the parameters from IoUT devices to a centralized FL machine, base station like devices are needed. Therefore, a joint cell association and resource allocation (JCARA) method is required and it is designed inspired by multi-agent deep deterministic policy gradient (MADDPG) to deal with distributed situations and unexpected time-varying states. The performance evaluation results show that our proposed MADDPG based algorithm achieves 80% and 41% performance improvements than standard actor-critic and DDPG, respectively, in terms of downlink throughput.

Mobile-edge computing (MEC) and nonorthogonal multiple access (NOMA) have been regarded as promising technologies for beyond fifth-generation (B5G) and sixth-generation (6G) networks. This study aims to reduce the computational overhead (weighted sum of consumed energy and latency) in a NOMA-assisted MEC network by jointly optimizing the computation offloading policy and channel resource allocation under dynamic network environments with time-varying channels. To this end, we propose a deep reinforcement learning algorithm named ACDQN that utilizes the advantages of both actor-critic and deep Q-network methods and provides low complexity. The proposed algorithm considers partial computation offloading, where users can split computation tasks so that some are performed on the local terminal while some are offloaded to the MEC server. It also considers a hybrid multiple access scheme that combines the advantages of NOMA and orthogonal multiple access to serve diverse user requirements. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 10%, 27%, and 69% lower computational overhead than the prevalent schemes, such as full offloading with NOMA, random offloading with NOMA, and fully local execution, and achieves near-optimal performance.

With the current state of development in demand response (DR) programs in smart grid systems, there have been great demands for automated energy scheduling for residential customers. Recently, energy scheduling in smart grids have focused on the minimization of electricity bills, the reduction of the peak demand, and the maximization of user convenience. Thus, a user convenience model is proposed under the consideration of user waiting times, which is a nonconvex problem. Therefore, the nonconvex is reformulated as convex to guarantee optimal solutions. Moreover, mathematical formulations for DR optimization are derived based on the reformulated convex problem. In addition, two types of pricing policies for electricity bills are designed in the mathematical formulations, i.e., real-time pricing policy and progressive policy. With real-time pricing policy, convexity is guaranteed whereas progressive policy cannot. Then, heuristic algorithms are finally designed for obtaining approximated optimal solutions in progressive policy.

The Internet of Things (IoT) is expected to play an important role in the construction of next generation mobile communication services, and is currently used in various services. However, the power-hungry battery significantly limits the lifetime of IoT devices. Among the various lifetime extension techniques, this paper discusses mobile charging, which enables wireless power transfer based on radio frequency with mobile chargers (MCs). MCs function as traveling target IoT networks that provide energy to battery-operated IoT devices. However, MCs with an energy-constrained battery result in limitation of travel-time. This paper formulates a problem to minimize energy consumption for charging IoT devices by determining the path of motion of an MC and efficient charging points, and proves that the problem is NP-hard. An efficient algorithm, named best charging efficiency (BCE), is proposed to solve the problem and the upper bound of the BCE algorithm is guaranteed using the duality of linear programming. In addition, an improved BCE algorithm called branching second best efficiency algorithm with additional searching techniques is introduced. Finally, this paper analyzes the difference in performance among the proposed algorithms, optimal solutions, and the existing algorithm and concludes that the performance of the proposed algorithm is near optimal, within 1% of difference ratio in terms of charging efficiency and delay.