Dimensionality reduction for images of IoT using machine learning
Abstract Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this re...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-57385-4 |
| _version_ | 1827301213179215872 |
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| author | Ibrahim Ali Khaled Wassif Hanaa Bayomi |
| author_facet | Ibrahim Ali Khaled Wassif Hanaa Bayomi |
| author_sort | Ibrahim Ali |
| collection | DOAJ |
| description | Abstract Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task’s accuracy. |
| first_indexed | 2024-04-24T16:19:09Z |
| format | Article |
| id | doaj.art-db40c318efec4793af43de7a8097e398 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-24T16:19:09Z |
| publishDate | 2024-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-db40c318efec4793af43de7a8097e3982024-03-31T11:19:15ZengNature PortfolioScientific Reports2045-23222024-03-0114111310.1038/s41598-024-57385-4Dimensionality reduction for images of IoT using machine learningIbrahim Ali0Khaled Wassif1Hanaa Bayomi2Computer Science Department, Faculty of Computers and Artificial Intelligence, Cairo UniversityComputer Science Department, Faculty of Computers and Artificial Intelligence, Cairo UniversityComputer Science Department, Faculty of Computers and Artificial Intelligence, Cairo UniversityAbstract Sensors, wearables, mobile devices, and other Internet of Things (IoT) devices are becoming increasingly integrated into all aspects of our lives. They are capable of gathering enormous amounts of data, such as image data, which can then be sent to the cloud for processing. However, this results in an increase in network traffic and latency. To overcome these difficulties, edge computing has been proposed as a paradigm for computing that brings processing closer to the location where data is produced. This paper explores the merging of cloud and edge computing for IoT and investigates approaches using machine learning for dimensionality reduction of images on the edge, employing the autoencoder deep learning-based approach and principal component analysis (PCA). The encoded data is then sent to the cloud server, where it is used directly for any machine learning task without significantly impacting the accuracy of the data processed in the cloud. The proposed approach has been evaluated on an object detection task using a set of 4000 images randomly chosen from three datasets: COCO, human detection, and HDA datasets. Results show that a 77% reduction in data did not have a significant impact on the object detection task’s accuracy.https://doi.org/10.1038/s41598-024-57385-4Edge computingDeep learningIoTAutoencoder |
| spellingShingle | Ibrahim Ali Khaled Wassif Hanaa Bayomi Dimensionality reduction for images of IoT using machine learning Scientific Reports Edge computing Deep learning IoT Autoencoder |
| title | Dimensionality reduction for images of IoT using machine learning |
| title_full | Dimensionality reduction for images of IoT using machine learning |
| title_fullStr | Dimensionality reduction for images of IoT using machine learning |
| title_full_unstemmed | Dimensionality reduction for images of IoT using machine learning |
| title_short | Dimensionality reduction for images of IoT using machine learning |
| title_sort | dimensionality reduction for images of iot using machine learning |
| topic | Edge computing Deep learning IoT Autoencoder |
| url | https://doi.org/10.1038/s41598-024-57385-4 |
| work_keys_str_mv | AT ibrahimali dimensionalityreductionforimagesofiotusingmachinelearning AT khaledwassif dimensionalityreductionforimagesofiotusingmachinelearning AT hanaabayomi dimensionalityreductionforimagesofiotusingmachinelearning |