Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

Cheng, Cheng; Guo, Liang; Wu, Tong; Sun, Jinlong; Gui, Guan; Adebisi, Bamidele; Gacanin, Haris; Sari, Hikmet

doi:10.1109/jiot.2021.3060904

Cited by 16 publications

(9 citation statements)

References 53 publications

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“…There are a variety of linear models applied for the tasks of prediction, including classification, probability estimation and regression. In ATC system linear models like the least-squares method (a mathematical regression analysis) is used in [2], Support Vector Machine (which best segregates two or more classes using a hyper-plane) is used in [25].…”

Section: Linear Geometric Modelsmentioning

confidence: 99%

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Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Jamal,

ashraf,

Jhanjhi

2023

Preprint

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Over the last few decades, there is a remarkable increase in air traffic worldwide; however, despite of rapid technological development and improved airport infrastructure, there is still an increasing number of aviation accidents and fatalities. In order to meet safety demands of Air Traffic Control (ATC), Machine Learning (ML) based aircraft conflict detection techniques are focused to predict events before they come to reality. This research presents a systematic literature review (SLR) about the applications of ML techniques in safety prediction of ATC system. Initially, the existing aircraft conflict prediction models are classified into three broad categories; logical, geometric and probabilistic. A formal process of SLR is then conducted which is initiated with preprocessing and effective searching. A series of filtering called title, abstract and objective-based are then applied to extract the most relevant articles. Detailed and critical reviews of selected articles are performed to identify challenges which are segregated into dataset, model and evaluation techniques, furthermore, the optimal solutions for each are proposed. Finally the matrices to map the identified challenged with their proposed solutions are constructed. This research serves as a foundation for future research and towards integration of ML based safety prediction techniques into modern ATC system.

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Section: Linear Geometric Modelsmentioning

confidence: 99%

“…The research work in [2] predicts trajectories of the aerial vehicles by a grouping-based conflict detection algorithm which is a based on ML. The preprocessed ADS-B dataset is used in this research.…”

Section: Detailed Literature Reviewmentioning

confidence: 99%

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Jamal,

ashraf,

Jhanjhi

2023

Preprint

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“…Owing to the rapid growth of machine learning (ML), many difficult research issues have been solved by MLbased methods, especially in future communication systems [6]. Thus, various ML-based approaches for wireless communication have been proposed in recent years [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. For example, in [8], the authors used an ML-aided method to predict trajectory and detect conflict for aerial vehicles.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, various ML-based approaches for wireless communication have been proposed in recent years [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23]. For example, in [8], the authors used an ML-aided method to predict trajectory and detect conflict for aerial vehicles. In [9], the authors proposed an ML-based method to solve the resource allocation problem in cognitive radio systems.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks

Shan

Zhao

Temma

et al. 2022

Wireless Communications and Mobile Computing

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Cooperative communication is widely seen as a promising key technology for improving the energy efficiency of battery-driven multiple mobile terminals (MTs). In this study, we investigate the use of machine learning (ML) in multiuser cooperative access networks. Because MT cooperation and bandwidth allocation are considered two main issues in such networks, we design an ML-aided method to solve the bandwidth issues so that the proposed method can maximize the network’s energy efficiency. Specifically, we use machine learning with artificial neural network (ANN) trained at base station (BS) (a) to decide whether MTs in the heterogeneous access network should cooperatively communicate and (b) to determine the optimal bandwidth allocation for this communication by distributing the trained ANN to all MTs. The computer simulation results show that under the described communication environment in this paper, the proposed method can provide 99.8% correct prediction for MT cooperation and output the optimal bandwidth allocation with at least 88% accuracy, which demonstrates the effectiveness of the proposed method. Besides, the simulations also show that the proposed method can provide about 14%–25% power consumption reduction, which validates the EE performance of the proposed method.

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“…However, in practice, the spectrum allocation problem and offloading strategy problem may be an NP-hard problem, due to dynamic change for a large number of mobile devices and the network environment, so that the conventional mathematical methods may be difficult to solve the optimization problem. With the rise of artificial intelligence technology, more and more researchers are currently focusing on using machine learning to solve complex optimization problems [27], [28]. Among them, the model-free reinforcement learning algorithm can be used to achieve the optimal strategy in solving many discrete and non-convex decision-making problems.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobile Edge Computing Networks for Internet of Things

Chen

Kuang

Zhu³

et al. 2021

IEEE Access

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In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence of eavesdropping environments, where there are multiple users who can offload their confidential tasks to the computational access point (CAP) for the assistance of computation. One unmanned aerial vehicle (UAV) attacker exists in the system and it can listen to the confidential data transmission from the users to the CAP. We optimize the system design of the intelligent MEC network, by adaptively allocating the offloading ratio and wireless bandwidth, to reduce the linearly weighted cost of the latency as well as energy consumption (EnC). Specifically, starting from the deep reinforcement learning, we devise a deep Q-network (DQN) network to adjust the offloading ratio and transmission bandwidth, which can help calculate the computational tasks and suppress the eavesdropping from the UAV efficiently. We finally provide some simulation results to validate the proposed offloading strategy. In particular, the proposed offloading strategy can achieve a much lower cost compared to the conventional ones, in the terms of latency and EnC.INDEX TERMS Deep reinforcement learning, Internet of Things, mobile edge computing, task offloading, unmanned aerial vehicles.

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Machine-Learning-Aided Trajectory Prediction and Conflict Detection for Internet of Aerial Vehicles

Cited by 16 publications

References 53 publications

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Machine Learning-based Aircraft Conflict Prediction: A Systematic Literature Review

Machine Learning-Aided Energy Efficiency Strategy for Multiuser Cooperative Networks

Intelligent Mobile Edge Computing Networks for Internet of Things

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