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

Abstract

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.

Quadcopter Drone for Vision-Based Autonomous Target Following

“…The green solid line shows the planned flight path, the blue dotted line shows the flight result estimated by the extended Kalman filter (EKF), and the red dotted line presents the GPS measurement result. It verifies that under the interference of strong wind, the offset of the UAV flight path and the planned path is satisfactorily less than 0.2 m under the help of the real-time kinematic positioning (RTK) technique [23].…”

“…This article Indoor, outdoor (low altitude), stairwell Identify by clothing; available for crowd, tracking, and obstacle avoidance [15] Indoor, outdoor (low altitude) QR-code identification (strict conditions), tracking but no obstacle avoidance [23] Outdoor (high altitude) Recognition and tracking functions are used for drones to park on the landing zone on the roof of the car [24] Outdoor (high altitude) UAVs identify and track targets at a high altitude [26] Indoor Use 3D vision to measure the distance between UAVs and objects (can be used for obstacle avoidance) [28] Outdoor Using deep learning and simulating multiple UAV tracking and obstacle avoidance (simulation only)…”

“…In addition, in order to improve the research quality, we also refer to other UAV and tracking-related literature, such as [23], which mainly discusses the deep learning application of UAVs in Internet of Things applications and investigates the related applications of UAVs in detail. Ref.…”

Industrial revolution and environmental sustainability: an analytical interpretation of research constituents in Industry 4.0

“…Industry 4.0 is, in a nutshell, the use of cutting-edge technologies like AI, robotics, quantum computing, additive manufacturing and the IoT in traditional production methods. Industry 4.0 and other ideas about smart manufacturing are becoming more and more critical in all areas of research and development (Heidari et al , 2022). Industry 4.0 allows focusing on essential functional pillars like technological progress, integration and collaboration and process optimization.…”

Electrophysiology and hyperscanning applied to e-learning for organizational training

“…Despite its novelty, this research presents the following strengths and weaknesses. Indeed, despite the extended development of informatics methods for sustainable computing and machine learning applications (Heidari et al, 2023a(Heidari et al, , 2023b, the employment of multiple (eight) wearable EEGs to assess a remote training session in the neuroscientific field represents a novelty. Also, great attention was dedicated to preserving ecological validity by involving an actual trainer and real trainees.…”