This book aims to provide a vision that can combine the best of both Artificial Intelligence (AI) and communication networks for designing the deployment trajectory to establish flexible Unmanned Aerial Vehicles (UAV) communication networks.This book will discuss the major challenges that can face deploying unmanned aerial vehicles in emergent networks. It will focus on possible applications of UAV in a Smart City environment where they can be supported by Internet of Things (IoT), wireless sensor networks, as well as 5G, and beyond. This book presents the possible problems and solutions, the network integration of the UAV and compare the communication technologies to be used.This book will be a collection of original contributions regarding state of the art AI/ML based solutions in UAV communication networks which can be used for routing protocol design, transport layer optimization, user/application behaviour prediction, communication network optimization, security, and anomaly detection.

Unmanned aerial vehicles (UAVs) and artificial intelligence (AI) are gaining the attention of academic and industrial researchers due to the freedoms that UAVs afford when operating and monitoring activities remotely. Applying machine learning and deep learning techniques can result in fast and reliable outputs and have helped in real-time monitoring, data collection and processing, and prediction. UAVs utilizing these techniques can become instrumental tools for computer/wireless networks, smart cities, military applications, agricultural sectors, and mining. Unmanned Aerial Vehicles and Multidisciplinary Applications Using AI Techniques is an essential reference source that covers pattern recognition, machine and deep learning-based methods, and other AI techniques and the impact they have when applied to different real-time applications of UAVs. It synthesizes the scope and importance of machine learning and deep learning models in enhancing UAV capabilities, solutions to problems, and numerous application areas. Covering topics such as vehicular surveillance systems, yield prediction, and human activity recognition, this premier reference source is a comprehensive resource for computer scientists; AI engineers; data scientists; agriculturalists; government officials; military leaders; business managers and leaders; students and faculty of higher education; academic libraries; academicians; and researchers in computer science, computer vision, pattern recognition, imaging, and engineering.

Unmanned Aerial Systems: Theoretical Foundation and Applications presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more. This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners. Covers some of the most innovative approaches to drones Provides the latest state-of-the-art research and development surrounding unmanned aerial systems Presents a comprehensive reference on unmanned aerial systems, with a focus on cutting-edge technologies and recent research trends in the area

This book addresses the major challenges in realizing unmanned aerial vehicles (UAVs) in IoT-based Smart Cities. The challenges tackled vary from cost and energy efficiency to availability and service quality. The aim of this book is to focus on both the design and implementation aspects of the UAV-based approaches in IoT-enabled smart cities’ applications that are enabled and supported by wireless sensor networks, 5G, and beyond. The contributors mainly focus on data delivery approaches and their performability aspects. This book is meant for readers of varying disciplines who are interested in implementing the smart planet/environments vision via wireless/wired enabling technologies. Involves the most up to date unmanned aerial vehicles (UAV) assessment and evaluation approaches Includes innovative operational ideas in agriculture, surveillance, rescue, etc. Pertains researchers, scientists, engineers and practitioners in the field of smart cities, IoT, and communications Fadi Al-Turjman received his Ph.D. from Queen’s University, Canada. He is a full professor and a research center director at Near East University, Nicosia. He is a leading authority in the area of IoT and intelligent systems. His publication history spans over 250 publications in addition to his editorialship in top journals such as the IEEE Communication Surveys and Tutorials, and the Elsevier Sustaibable Cities and Society.

Applications of Machine Learning in UAV Networks presents a pioneering exploration into the symbiotic relationship between machine learning techniques and UAVs. In an age where UAVs are revolutionizing sectors as diverse as agriculture, environmental preservation, security, and disaster response, this meticulously crafted volume offers an analysis of the manifold ways machine learning drives advancements in UAV network efficiency and efficacy. This book navigates through an expansive array of domains, each demarcating a pivotal application of machine learning in UAV networks. From the precision realm of agriculture and its dynamic role in yield prediction to the ecological sensitivity of biodiversity monitoring and habitat restoration, the contours of each domain are vividly etched. These explorations are not limited to the terrestrial sphere; rather, they extend to the pivotal aerial missions of wildlife conservation, forest fire monitoring, and security enhancement, where UAVs adorned with machine learning algorithms wield an instrumental role. Scholars and practitioners from fields as diverse as machine learning, UAV technology, robotics, and IoT networks will find themselves immersed in a confluence of interdisciplinary expertise. The book's pages cater equally to professionals entrenched in agriculture, environmental studies, disaster management, and beyond.

The book discusses how Unmanned Aerial Vehicles (UAVs) can leverage the sub-6 GHz massive MIMO to address cell selection and interference issues in future wireless networks. The book takes a close look at utilizing UAVs to achieving direct and efficient device-to device (D2D) communications in the sky. Also, the key 6G enablers (cell-free architectures, artificial intelligence, reconfigurable intelligent surfaces, THz communications, and non-terrestrial networks) for UAV communication are broached, and the primary technological challenges of each enabler are discussed extensively. Furthermore, the book covers the design of adaptable UAVs to operate in diverse and harsh environmental conditions. Additionally, the existing UAVs’ networking protocols and how these can be greatly enhanced to address the issue of intermittent network changes and channel impairments are discussed. The prospects and societal benefits envisioned in future UAVs are also presented.

This book offers a comprehensive analysis of the theory and tools needed for the development of an efficient and robust infrastructure for the design of collaborative patrolling unmanned aerial vehicle (UAV) swarms, focusing on its applications for tactical intelligence drones. It discusses frameworks for robustly and near-optimally analyzing flocks of semi-autonomous vehicles designed to efficiently perform the ongoing dynamic patrolling and scanning of pre-defined “search regions”. It discusses the theoretical limitations of such systems, as well as the trade-offs between the systems’ various economic and operational parameters. Current UAV systems rely mainly on human operators for the design and adaptation of drones’ flying routes. However, recent technological advances have introduced new systems, comprised of a small number of self-organizing vehicles, manually guided at the swarm level by a human operator. With the growing complexity of such man-supervised architectures, it is becoming increasingly harder to guarantee a pre-defined level of performance. The use of large scale swarms of UAVs as a combat and reconnaissance platform therefore necessitates the development of an efficient optimization mechanism of their utilization, specifically in the design and maintenance of their patrolling routes. The book is intended for researchers and engineers in the fields of swarms systems and autonomous drones.