Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. However, the cooperation of small and mini drones in a network is capable of further improving the performance and the coverage area of UAVs. There are numerous new challenges to be solved before the widespread introduction of multi-UAV-based heterogeneous flying ad hoc networks (FANET), including the formulation of a stable network structure. Meanwhile, an efficient gateway-selection algorithm and management mechanism is required. However, the stability control of the hierarchical UAV network guarantees the efficient collaboration of the drones. In this article, we begin by surveying the FANET structure and its protocol architecture. Then, a variety of distributed gateway-selection algorithms and cloud-based stability-control mechanisms are addressed, complemented by a range of open challenges.

Unmanned aerial vehicles (UAVs) have stroke great interested both by the academic community and the industrial community due to their diverse military applications and civilian applications. Furthermore, UAVs are also envisioned to be part of future airspace traffic. The application functions delivery relies on information exchange among UAVs as well as between UAVs and ground stations (GSs), which further closely depends on aeronautical channels. However, there is a paucity of comprehensive surveys on aeronautical channel modeling in line with the specific aeronautical characteristics and scenarios. To fill this gap, this paper focuses on reviewing the air-to-ground (A2G), ground-to-ground (G2G), and air-to-air (A2A) channel measurements and modeling for UAV communications and aeronautical communications under various scenarios. We also provide the design guideline for managing the link budget of UAV communications taking account of link losses and channel fading effects. Moreover, we also analyze the receive/transmit diversity gain and spatial multiplexing gain achieved by multiple-antenna-aided UAV communications. Finally, we discuss the remaining challenge and open issues for the future development of UAV communication channel modeling.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet of Things (IoT) networks. Considering the substantially limited processing capability, IoT devices may not be able to tackle with heavy computing tasks. In this paper, a multi-UAV aided mobile edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE) based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency for the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance oriented UAV deployment scheme as well as the task scheduling algorithm.

Internet of Vehicles (IoV) has drawn great interests recent years. Various IoV applications have emerged for improving the safety, efficiency and comfort on the road. Cloud computing constitutes a popular technique for supporting delay-tolerant entertainment applications. However, for advanced latency-sensitive applications (e.g., auto/assisted driving, emergency failure management), cloud computing may result in excessive delay. Edge computing, which extends computing and storage capabilities to the edge of the network, emerges as an attractive technology. Therefore, to support these computationally intensive and latency-sensitive applications in IoVs, in this paper, we integrate mobile edge computing nodes (i.e., mobile vehicles) and fixed edge computing nodes (i.e., fixed road infrastructures) to provide low-latency computing services cooperatively. For better exploiting these heterogeneous edge computing resources, the concept of software-defined networking (SDN) and edge computing aided IoV (EC-SDIoV) is conceived. Moreover, in a complex and dynamic IoV environment, the outage of both processing nodes and communication links becomes inevitable, which may have life-threatening consequences. In order to ensure the completion with high reliability of latency-sensitive IoV services, we introduce both partial computation offloading and reliable task allocation with reprocessing mechanism to EC-SDIoV. Since the optimization problem is non-convex and NP-hard, a heuristic algorithm, fault-tolerant particle swarm optimization algorithm is designed for maximizing the reliability (FPSO-MR) with latency constraints. Performance evaluation results validate that the proposed scheme is indeed capable of reducing the latency as well as improving the reliability of the EC-SDIoV.

Unmanned aerial vehicles (UAVs) have been widely used in both military and civilian applications. Equipped with diverse communication payloads, UAVs cooperating with satellites and base stations (BSs) constitute a space-air-ground three-tier heterogeneous network, which are beneficial in terms of both providing the seamless coverage as well as of improving the capacity for increasingly prosperous Internet of Things (IoT) networks. However, cross-tier interference may be inevitable among these tightly embraced heterogeneous networks when sharing the same spectrum. The power association problem in satellite, UAV and macrocell three-tier networks becomes a critical issue. In our paper, we propose a two-stage joint hovering altitude and power control solution for the resource allocation problem in UAV networks considering the inevitable cross-tier interference from space-air-ground heterogeneous networks. Furthermore, Lagrange dual decomposition and concave-convex procedure (CCP) method are used to solve this problem, followed by a low-complexity greedy search algorithm. Finally, simulation results show the effectiveness of our proposed two-stage joint optimization algorithm in terms of UAV network’s total throughput.