Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach

Lin, Yun; Wang, Meiyu; Zhou, Xianglong; Ding, Guoru; Mao, Shiwen

doi:10.1109/tccn.2020.2973376

Cited by 124 publications

(36 citation statements)

References 34 publications

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“…During the recent years, deep learning based methods have made remarkable progress in many fileds [24], such as Internet of Things [25,26], Signal processing [27,28], UAV [29], wireless communications [30], and especially in the field of agriculture [31][32][33][34][35]. These include fruit classification [36][37][38], yield estimation and counting [39,40].…”

Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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Fruit detection in orchards is one of the most crucial tasks for designing the visual system of an automated harvesting robot. It is the first and foremost tool employed for tasks such as sorting, grading, harvesting, disease control, and yield estimation, etc. Efficient visual systems are crucial for designing an automated robot. However, conventional fruit detection methods always a trade-off with accuracy, real-time response, and extensibility. Therefore, an improved method is proposed based on coarse-to-fine multitask cascaded convolutional networks (MTCNN) with three aspects to enable the practical application. First, the architecture of Fruit-MTCNN was improved to increase its power to discriminate between objects and their backgrounds. Then, with a few manual labels and operations, synthetic images and labels were generated to increase the diversity and the number of image samples. Further, through the online hard example mining (OHEM) strategy during training, the detector retrained hard examples. Finally, the improved detector was tested for its performance that proved superior in predicted accuracy and retaining good performances on portability with the low time cost. Based on performance, it was concluded that the detector could be applied practically in the actual orchard environment.

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Section: Deep Learning Based Methodsmentioning

confidence: 99%

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Zhang¹,

Ren²,

Tao³

et al. 2021

KSII TIIS

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show abstract

“…Due to their high maneuverability and flexible deployment, UAVs have attracted a lot of interest in recent years [16]. In the field of communication, a UAV could act as an aerial BS to provide network connectivity for ground users [17], or coordinate with other UAVs to form ad hoc networks and share information with each other [18], [19]. Moreover, as the reliability of their own state estimation and control continues to improve [20], [21], UAVs are capable of performing tasks with requirements for high reliability like reliable video transmission [22].…”

Section: A Motivationmentioning

confidence: 99%

Toward Reliable UAV-Enabled Positioning in Mountainous Environments: System Design and Preliminary Results

Wang

Liu

et al. 2022

IEEE Trans. Rel.

Self Cite

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Reliable positioning services are extremely important for users in mountainous environments. However, in such environments, the service reliability of conventional wireless positioning technologies is often disappointing due to frequent nonline-of-sight (NLoS) propagation and poor geometry of available anchor nodes. Hence, we propose a unmanned aerial vehicle (UAV)-enabled positioning system that utilizes UAV's mobility to overcome the above challenges. In this article, we first analyze and model the major causes of service failures in the proposed system. In particular, a geometry-based NLoS probability model is established based on the digital elevation models (DEM) of realistic terrain for reliability analysis. Subsequently, we propose a reliability prediction method and derive the corresponding metric to evaluate the system's ability to provide reliable positioning services. Moreover, we also develop a voting-based method for the further enhancement of service reliability. Monte-Carlo simulations show that in mountainous environments, the proposed reliability prediction method could achieve a prediction accuracy that is at least 36.8% higher than that of the existing technique. In addition, in the experiments conducted in two typical valley scenarios, the proposed reliability enhancement method improves the service reliability of the proposed system by 23% and 29%, respectively. These numerical results demonstrate the strong potential of the proposed system and methods for reliable positioning.

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“…The authors in [156] investigate the dynamic management of information exchange in a UAV network by studying two different information sharing schemes: the dynamic channel model and the dynamic time slot model. In the first scheme, the channel is shared for the exchange of information in the same time slot, while in the second model, time slots are shared according to the priority level.…”

Section: ) Update Rulementioning

confidence: 99%

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

Lahmeri

Kishk

Alouini

2021

IEEE Open J. Commun. Soc.

111

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Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for the next-generation wireless communication networks. Their mobility and their ability to establish line of sight (LOS) links with the users made them key solutions for many potential applications. In the same vein, artificial intelligence (AI) is growing rapidly nowadays and has been very successful, particularly due to the massive amount of the available data. As a result, a significant part of the research community has started to integrate intelligence at the core of UAVs networks by applying AI algorithms in solving several problems in relation to drones. In this article, we provide a comprehensive overview of some potential applications of AI in UAV-based networks. We also highlight the limits of the existing works and outline some potential future applications of AI for UAVs networks.

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Dynamic Spectrum Interaction of UAV Flight Formation Communication With Priority: A Deep Reinforcement Learning Approach

Cited by 124 publications

References 34 publications

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

High-Quality Coarse-to-Fine Fruit Detector for Harvesting Robot in Open Environment

Toward Reliable UAV-Enabled Positioning in Mountainous Environments: System Design and Preliminary Results

Artificial Intelligence for UAV-Enabled Wireless Networks: A Survey

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