Autonomous Airborne Wireless Networks. Группа авторов

UAVs and the different system requirements of the UAV system. Afterward, the chapter discusses the design consideration of UAV networks in the context of disaster recovery networks. New technologies and infrastructure trends for UAV disaster networks namely, network function virtualization (NFV), software‐defined networks (SDN), cloud computing, and millimeter‐wave networks are also discussed in the chapter. Further, the authors discuss the enhancement in technologies such as artificial intelligence, machine learning, optimization theory, and game theory as they impact the overall performance of the UAV‐enabled disaster recovery networks. Finally, the chapter presents the research trends and some insight into the future.

      In Chapter 10, the authors discuss the importance of UAVs in monitoring COVID‐19 restrictions of social distancing, public gatherings, and physical contacts in a smart city environment. The chapter starts with a review of recent literature addressing the impact of COVID‐19 in the current scenario and strategies to find potential solutions with existing communication and computing technologies. Afterward, the authors present two use case scenarios of UAVs namely, UAVs as aerial base stations (ABS) and UAVs as Relays, while including the simulation setups with ray tracing for both scenarios. The chapter then presents the derivation of the optimal number of ABSs to cover a geographical region, given the constraint on ABS transmission power, the altitude of hovering, and including the path loss and channel fading effects from ray‐tracing simulations. The authors then describe the 5G air interface when using the UAVs as relays. Finally, simulation results on the received power by the ground users and the throughput coverage area are presented.

      In Chapter 11, the authors present and discuss both the research initiatives and the scientific literature on IoT‐based smart farming (SF), especially the use of UAVs in SF. The authors start by presenting an analysis of how UAVs are used in SF and the application scenarios. This is then followed by a detailed review of the scientific work in the literature highlighting the role of unmanned vehicles. The chapter then presents both the requirements and solutions for networking and a brief comparison of the existing protocol supporting IoT scenarios in agricultural settings. Finally, the chapter discusses the potential future role of the joint use of mobile edge computing (MEC) and the 5G network, presenting network architecture to connect smart farms through UAVs and satellites.

Wetlands monitoring requires accurate topographic and bathymetric maps, and this can be achieved using UAVs that can create maps regularly, with minimum cost and reduced environmental impact. Chapter 12 introduces a set of systems needed to create this automation. The chapter starts by discussing the automated image labeling system. Next, the authors present an online classification system for differentiating land and water. The authors then present offline bathymetric map creation using aerial robots. Since the offline approach does not take full advantage of the adaptability that the UAV provides, the authors present the online bathymetric mapping. Finally, the chapter presents results and analysis to show the best combination of the online bathymetric mapping.

      Integration of terrestrial and satellite networks has been proposed for leveraging the combined benefits of both complementary technologies. Moreover, with the quest of exploring deep space and connecting solar system planets with the Earth, the traditional satellite network has gone beyond the geosynchronous equatorial orbit (GEO) wherein Interplanetary Internet will play a key role. Chapter 13 presents a short review of the inter‐satellite and deep space network (ISDSN). This chapter discusses the classification of the ISDSN into different tiers while highlighting the communication and networking paradigms. Further, the chapter also discusses the security requirements, challenges, and threats in each tier. The potential solutions to the identified challenges at the different tiers of the ISDSN are also described. Finally, the chapter concludes by highlighting the crucial role of the ISDSN in future cellular networks.

2 Channel Model for Airborne Networks

       Aziz A. Khuwaja1,2 and Yunfei Chen1

       1School of Engineering, Electrical and Electronic Engineering Stream, University of Warwick, Coventry, UK

       2Department of Electrical Engineering, Sukkur IBA University, Sukkur, Pakistan

      

2.1 Introduction

      The use of unmanned aerial vehicles (UAVs) is desirable due to their high maneuverability, ease of operability, and affordable prices in various civilian applications, such as disaster relief, aerial photography, remote surveillance, and continuous telemetry. One of the promising application of UAVs is enabling the wireless communication network in cases of natural calamity and in hot spot areas during peak demand where the resources of the existing communication network have been depleted [1]. Qualcomm has already initiated field trials for the execution of fifth generation (5G) cellular applications [2]. Google and Facebook are also exploiting the use of UAVs to provide Internet access to far‐flung destinations [3].

      The selection of an appropriate type of UAV is essential to meet the desired quality of service (QoS) depending on applications and goals in different environments. In fact, for any specific wireless networking application, the UAV altitude and its capabilities must be taken into account. UAVs can be categorized, based on their altitude, into low‐altitude platforms (LAPs) and high‐altitude platforms (HAPs). Furthermore, based on their structure, UAVs can be categorized as fixed‐wing and rotary‐wing UAVs. In comparison with rotary wings, fixed‐wing UAVs move in the forward direction to remain aloft, whereas rotary‐wing UAVs are desired for applications that require UAVs to be quasi‐stationary over a given area. However, in both types, flight duration depends on their energy sources, weight, speed, and trajectory.

The salient features of UAV‐based communication network are the air‐to‐ground (AG) and air‐to‐air (AA) propagation channels. Accurate channel modeling is imperative to fulfill the ever‐increasing requirements of end users to transfer data at higher rates. The available channel models for AG propagation are designed either for terrestrial communication or for aeronautical communications at higher altitudes. These models are not preferable for low‐altitude UAV communication, which uses small size UAVs in different urban environments. On one hand, the AG channel exhibits higher probability of line‐of‐sight (LoS) propagation, which reduces the transmit power requirement and provides higher link reliability. In cases with non‐line‐of‐sight (NLoS), shadowing and diffraction losses can be compensated with a large elevation angle between the UAV and the ground device. On the other hand, UAV mobility can incur significant temporal variations in both the AG and AA propagation due to the Doppler shift.

      Small UAVs may experience airframe shadowing due to their flight path with sharper changes in pitch, yaw, and roll angle. In addition, distinct structural design and material of UAV body may contribute additional shadowing attenuation. This phenomenon has not yet been extensively studied in the literature.

      Despite the number of promising UAV applications, one must address several technical challenges before the widespread applicability of UAVs. For example, while using UAV in aerial base station (BS) scenario, the important design considerations include radio resource management, flight time, optimal three‐dimensional deployment of UAV, trajectory optimization, and performance analysis. Meanwhile, considering UAV in the aerial user equipment (UE) scenario, the main challenges include interference management, handover management, latency control, and three‐dimensional localization. However, in both scenarios, channel modeling is an important design step in the implementation of UAV‐based communication network. This chapter provides an overview of the use of UAV as aerial UEs and aerial BSs and discusses the technical challenges related to AG channel modeling, airframe shadowing, optimal deployment of UAVs, trajectory optimization, resource management, and energy efficiency.