Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

被引:103
作者
Heidari, Arash [1 ]
Navimipour, Nima Jafari [2 ,5 ]
Unal, Mehmet [3 ]
Zhang, Guodao [4 ]
机构
[1] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran
[2] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, TR-34083 Istanbul, Turkiye
[3] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiye
[4] Hangzhou Dianzi Univ, Dept Digital Media Technol, Hangzhou 310018, Peoples R China
[5] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwan
关键词
Internet of Drones; IoD; review; UAV; Machine Learning; Deep Learning; OBJECT DETECTION; SMART CITIES; UAV NETWORKS; DEEP; MANAGEMENT; POWER; IDENTIFICATION; INTERFERENCE; OPTIMIZATION; CHALLENGES;
D O I
10.1145/3571728
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Deep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles ( UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.
引用
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页数:45
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