Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection.
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| Title: | Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection. |
|---|---|
| Authors: | Gumaei, Abdu1, Al-Rakhami, Mabrook1, Hassan, Mohammad Mehedi1, Pace, Pasquale2, Alai, Gianluca2, Lin, Kai3, Fortino, Giancarlo2 |
| Source: | IEEE Network. Mar/Apr2021, Vol. 35 Issue 1, p94-100. 7p. |
| Subjects: | Deep learning, Blockchains, Edge computing, 5G networks, Drone aircraft |
| Abstract: | Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using radio frequency (RF) signals and through 5G networks. Recently, few studies have been proposed to use deep neural networks (DNNs) on RF signals for identifying drones and detecting their flight modes, such as off, on and connected, flying, hovering, and video recording. However, transmitting RF signals between drones and 5G nodes needs to be secure and decentralized; in addition, the performance of identification and detection needs to be more accurate. In this article, we introduce a framework that combines a blockchain with a deep recurrent neural network (DRNN) and edge computing for 5G-enabled drone identification and flight mode detection. In the proposed framework, raw RF signals of different drones under several flight modes are remotely sensed and collected on a cloud server to train a DRNN model and then distribute the trained model on edge devices for detecting drones and their flight modes. Blockchain is used in the proposed framework for data integrity and securing data transmission. The DRNN model is evaluated on a public dataset, called DroneRF. Experimental evaluation results show that the DRNN model of the proposed framework can detect drones and their flight modes from real RF signals with high accuracy as compared to recent related work. [ABSTRACT FROM AUTHOR] |
| Copyright of IEEE Network is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 183403828 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Gumaei%2C+Abdu%22">Gumaei, Abdu</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Al-Rakhami%2C+Mabrook%22">Al-Rakhami, Mabrook</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Hassan%2C+Mohammad+Mehedi%22">Hassan, Mohammad Mehedi</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Pace%2C+Pasquale%22">Pace, Pasquale</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Alai%2C+Gianluca%22">Alai, Gianluca</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Lin%2C+Kai%22">Lin, Kai</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Fortino%2C+Giancarlo%22">Fortino, Giancarlo</searchLink><relatesTo>2</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IEEE+Network%22">IEEE Network</searchLink>. Mar/Apr2021, Vol. 35 Issue 1, p94-100. 7p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Blockchains%22">Blockchains</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%225G+networks%22">5G networks</searchLink><br /><searchLink fieldCode="DE" term="%22Drone+aircraft%22">Drone aircraft</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Nowadays, drones are not just deployed for defense and military establishments, but they are widely used in many applications such as natural disaster monitoring, soil and crop analysis, road and traffic surveillance, and consumer product delivery. Some information, such as drone identification and flight modes, can be transmitted to other drones. This information can be shared between drones by using radio frequency (RF) signals and through 5G networks. Recently, few studies have been proposed to use deep neural networks (DNNs) on RF signals for identifying drones and detecting their flight modes, such as off, on and connected, flying, hovering, and video recording. However, transmitting RF signals between drones and 5G nodes needs to be secure and decentralized; in addition, the performance of identification and detection needs to be more accurate. In this article, we introduce a framework that combines a blockchain with a deep recurrent neural network (DRNN) and edge computing for 5G-enabled drone identification and flight mode detection. In the proposed framework, raw RF signals of different drones under several flight modes are remotely sensed and collected on a cloud server to train a DRNN model and then distribute the trained model on edge devices for detecting drones and their flight modes. Blockchain is used in the proposed framework for data integrity and securing data transmission. The DRNN model is evaluated on a public dataset, called DroneRF. Experimental evaluation results show that the DRNN model of the proposed framework can detect drones and their flight modes from real RF signals with high accuracy as compared to recent related work. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IEEE Network is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1109/MNET.011.2000204 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 7 StartPage: 94 Subjects: – SubjectFull: Deep learning Type: general – SubjectFull: Blockchains Type: general – SubjectFull: Edge computing Type: general – SubjectFull: 5G networks Type: general – SubjectFull: Drone aircraft Type: general Titles: – TitleFull: Deep Learning and Blockchain with Edge Computing for 5G-Enabled Drone Identification and Flight Mode Detection. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Gumaei, Abdu – PersonEntity: Name: NameFull: Al-Rakhami, Mabrook – PersonEntity: Name: NameFull: Hassan, Mohammad Mehedi – PersonEntity: Name: NameFull: Pace, Pasquale – PersonEntity: Name: NameFull: Alai, Gianluca – PersonEntity: Name: NameFull: Lin, Kai – PersonEntity: Name: NameFull: Fortino, Giancarlo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar/Apr2021 Type: published Y: 2021 Identifiers: – Type: issn-print Value: 08908044 Numbering: – Type: volume Value: 35 – Type: issue Value: 1 Titles: – TitleFull: IEEE Network Type: main |
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