Mobile Edge Computing and Machine Learning in the Internet of Unmanned Aerial Vehicles: A Survey

Ning, Zhaolong; Hu, Hao; Wang, Xiaojie; Guo, Lei; Guo, Song; Wang, Guoyin; Gao, Xinbo

doi:10.1145/3604933

Cited by 36 publications

(11 citation statements)

References 139 publications

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“…This study examines the potential use of UAV resources for distributed data processing and real-time decision-making. The research emphasizes the significance of leveraging edge computing and machine learning to enhance efficiency and automate UAV operations in various sectors, including monitoring and rescue operations [7].…”

Section: State Of the Artmentioning

confidence: 99%

Method of comparing and transforming images obtained using UAV

Karapet,

Savitskyi,

Vakaliuk

2024

reks

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The subject matter of this article involves reviewing and developing methods for the comparison and transformation of images obtained using UAV via Computer Vision tools. The goal is to improve methods for image comparison and transformation. Various image-processing methods were employed to achieve the goal of this study,thereby contributing to the development of practical algorithms and approaches for image analysis and comparison. The tasks can be described as follows: 1) development of image comparison methods: design tools for the comparison of images from UAV that efficiently detect differences using algorithms such as cv2.absdiff and the PIL module; 2) Image transformation: implement transformation methods for images from UAV, including perspective transformation and thresholding, to enhance the quality and accuracy of image analysis. The methods used were algorithm development, image transformation methods, statistical analysis, experimental testing, and performance evaluation. The metrics used in this article are response time and accuracy. Algorithms for image comparison have also been refined, particularly those transformed through Global Threshold Value, Adaptive Mean Thresholding, and Adaptive Gaussian Thresholding. A novel change filtering method was introduced to enhance the precision of image comparison by filtering out insignificant alterations following image transformation. Comprehensive investigation of image comparison involving edge detection methods has been systematically presented. The results contain the development of practical algorithms and approaches for image analysis and comparison applicable in diverse areas such as military, security, and agriculture. Possibilities of applying our methods and algorithms in the context of drones were also considered, which is particularly relevant in tasks related to computer vision in unmanned aerial vehicles, where limited resources and the need for real-time processing of a large volume of data create unique challenges. Conclusions. The results contain OpenCV and PIL image comparison methods. OpenCV pixel-by-pixel comparison algorithm showed a better response time with the same accuracy. OpenCV method has 92,46% response time improvement compared with PIL and is 276ms. As for image thresholding with comparison, a method based on Global Threshold Value showed the shortest response time (266ms) and the lowest accuracy. The highest accuracy and response time (366ms) were obtained using the Adaptive Gaussian Thresholding method.

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Section: State Of the Artmentioning

confidence: 99%

Method of comparing and transforming images obtained using UAV

Karapet,

Savitskyi,

Vakaliuk

2024

reks

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“…Reference [16] introduces four novel feature quality metrics and utilizes these metrics to dynamically select useful features during the combination process. The literature [17] proposes a novel machine learning [18] feature selection method, called Unsupervised Discriminative Projection for Feature Selection (UDPFS) to select discriminative features by conducting fuzziness learning and sparse learning, simultaneously. In the literature [19], the Multiple Feature Extraction Extreme Learning Machine (MFE-ELM) algorithm is employed for cloud computing, adding a multi-feature extraction process to cloud servers and using the MFE-ELM algorithm deployed on cloud nodes to detect and discover network intrusions on the cloud nodes.…”

Section: Related Workmentioning

confidence: 99%

“…IoT-23 [18] is a large-scale dataset containing both normal and malicious network traffic in the context of the Internet of Things. It was released by the Stratosphere lab in 2020.…”

Section: Experimental Datamentioning

confidence: 99%

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data

Wang,

Xiong,

Chen

et al. 2023

Sensors

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The rapid growth of the Internet of Things (IoT) and big data has raised security concerns. Protecting IoT big data from attacks is crucial. Detecting real-time network attacks efficiently is challenging, especially in the resource-limited IoT setting. To enhance IoT security, intrusion detection systems using traffic features have emerged. However, these face difficulties due to varied traffic feature formats, hindering fast and accurate detection model training. To tackle accuracy issues caused by irrelevant features, a new model, LVW-MECO (LVW enhanced with multiple evaluation criteria), is introduced. It uses the LVW (Las Vegas Wrapper) algorithm with multiple evaluation criteria to identify pertinent features from IoT network data, boosting intrusion detection precision. Experimental results confirm its efficacy in addressing IoT security problems. LVW-MECO enhances intrusion detection performance and safeguards IoT data integrity, promoting a more secure IoT environment.

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“…To expand the field of view for hyperspectral data collection, UAV (Unmanned Aerial Vehicle) acquisition can be employed [11,12], wherein the hyperspectral image sensor is mounted on a UAV. The mobility and flexibility of UAVs enable them to offer various services to users on the ground [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation

Zheng,

Xiong,

et al. 2023

Electronics

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With the continuous advancement of remote sensing technology, the information encapsulated within hyperspectral images has become increasingly enriched. The effective and comprehensive utilization of spatial and spectral information to achieve the accurate classification of hyperspectral images presents a significant challenge in the domain of hyperspectral image processing. To address this, this paper introduces a novel approach to hyperspectral image classification based on geodesic spatial–spectral collaborative representation. It introduces geodesic distance to extract spectral neighboring information from hyperspectral images and concurrently employs Euclidean distance to extract spatial neighboring information. By integrating collaborative representation with spatial–spectral information, the model is constructed. The collaborative representation coefficients are obtained by solving the model to reconstruct the testing samples, leading to the classification results derived from the minimum reconstruction residuals. Finally, with comparative experiments conducted on three classical hyperspectral image datasets, the effectiveness of the proposed method is substantiated. On the Indian Pines dataset, the proposed algorithm achieved overall accuracy (OA) of 91.33%, average accuracy (AA) of 93.81%, and kappa coefficient (Kappa) of 90.13%. In the case of the Salinas dataset, OA was 95.62%; AA was 97.30%; and Kappa was 93.84%. Lastly, on the PaviaU dataset, OA stood at 95.77%; AA was 94.13%; and Kappa was 94.38%.

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Mobile Edge Computing and Machine Learning in the Internet of Unmanned Aerial Vehicles: A Survey

Cited by 36 publications

References 139 publications

Method of comparing and transforming images obtained using UAV

Method of comparing and transforming images obtained using UAV

Multi-Criteria Feature Selection Based Intrusion Detection for Internet of Things Big Data

Hyperspectral Image Classification Using Geodesic Spatial–Spectral Collaborative Representation

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