Overview

Unmanned aerial vehicles (UAVs) continue to become more advanced and complex as researchers push the boundaries of other supporting technologies. 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. Furthermore, the students and researchers finds knowledge that illuminates the convergence of UAVs and machine learning, arguably one of the most riveting frontiers in contemporary research.

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Jahan Hassan is a Senior Lecturer at the School of Engineering and Technology, Central Queensland University, Australia. She earned her PhD from the University of New South Wales (Sydney, Australia), and her Bachelor degree from Monash University (Australia), both in Computer Science. Dr Hassan has dedicated her work to developing unmanned aerial vehicle (UAV) networks for various applications, with a focus on improving efficiency and effective communication among UAVs. Her work has utilized advanced machine learning algorithms to optimize UAV movement and energy usage based on experience. Additionally, she has explored in-flight UAV recharging methods, using energy sources in the air. She is an Area Editor for Elsevier Ad Hoc Networks journal. She has served as Guest Editor for IEEE Communications Magazine, IEEE Network, Elsevier Ad Hoc Networks, and MDPI Drones. Dr Hassan is a Senior Member of the IEEE. Saeed Hamood Alsamhi received the B.Eng. degree from the Department of Electronic Engineering (Communication Division), IBB University, Yemen, in 2009, and the M.Tech. degree in communication systems and a Ph.D. degree from the Department of Electronics Engineering, Indian Institute of Technology (Banaras Hindu University), IIT (BHU), Varanasi, India, in 2012 and 2015. In 2009, he worked as a Lecturer Assistant in the Engineering faculty at IBB University. Afterwards, he held a postdoctoral position with the School of Aerospace Engineering, Tsinghua University, Beijing, China, in optimal and smart wireless network research and its applications to enhance robotics technologies. Since 2019, he has been an Assistant Professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen. In 2020, he worked as MSCA SMART 4.0 FELLOW at Athlone Institute of Technology, Athlone, Ireland. He is currently Senior Research Fellow at Insight Centre for Data Analytics, University of Galway, Ireland. He has published more than 160 articles in high-reputation journals in IEEE, Elsevier, Springer, Wiley, etc. publishers. His areas of interest include green and semantic communication, green Internet of Things, QoE, QoS, multi-robot collaboration, blockchain technology, federated learning, and space technologies (high altitude platforms, drones, and tethered balloon technologies).

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