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Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G
With the development of the Internet of Things (IoT), the number of drones, as a consumer-level IoT device, is rapidly increasing. The existence of a large number of drones increases the risk of misoperation during manual control. Therefore, it has become an inevitable trend to realize drone flying...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662669/ https://www.ncbi.nlm.nih.gov/pubmed/33143360 http://dx.doi.org/10.3390/s20216209 |
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author | Gao, Ying Liu, Yangliang Wen, Quansi Lin, Hongliang Chen, Yijian |
author_facet | Gao, Ying Liu, Yangliang Wen, Quansi Lin, Hongliang Chen, Yijian |
author_sort | Gao, Ying |
collection | PubMed |
description | With the development of the Internet of Things (IoT), the number of drones, as a consumer-level IoT device, is rapidly increasing. The existence of a large number of drones increases the risk of misoperation during manual control. Therefore, it has become an inevitable trend to realize drone flying automation. Drone flying automation mainly relies on massive drone applications and services as well as third-party service providers, which not only complicate the drone network service environment but also raise some security and privacy issues. To address these challenges, this article proposes an innovative architecture called Secure Drone Network Edge Service (SDNES), which integrates edge computing and blockchain into the drone network to provide real-time and reliable network services for drones. To design a feasible and rational SDNES architecture, we first consider the real-time performance and apply edge computing technology in it to provide low-latency edge services for drones under 5G mobile network. We use DAG-based blockchain to guarantee the security and reliability of the drone network service environment and effectively avoid malicious behaviors. In order to illustrate the feasibility of this architecture, we design and implement a specific service case named Drone Collision Avoidance Navigation Service based on SDNES. Finally, a simulation experiment for the specific service case and a series of other performance-related experiments were carried out to verify the feasibility and rationality of our proposed architecture. The experimental results demonstrate that SDNES is a promising architecture to assist and accelerate drone flying automation. |
format | Online Article Text |
id | pubmed-7662669 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-76626692020-11-14 Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G Gao, Ying Liu, Yangliang Wen, Quansi Lin, Hongliang Chen, Yijian Sensors (Basel) Article With the development of the Internet of Things (IoT), the number of drones, as a consumer-level IoT device, is rapidly increasing. The existence of a large number of drones increases the risk of misoperation during manual control. Therefore, it has become an inevitable trend to realize drone flying automation. Drone flying automation mainly relies on massive drone applications and services as well as third-party service providers, which not only complicate the drone network service environment but also raise some security and privacy issues. To address these challenges, this article proposes an innovative architecture called Secure Drone Network Edge Service (SDNES), which integrates edge computing and blockchain into the drone network to provide real-time and reliable network services for drones. To design a feasible and rational SDNES architecture, we first consider the real-time performance and apply edge computing technology in it to provide low-latency edge services for drones under 5G mobile network. We use DAG-based blockchain to guarantee the security and reliability of the drone network service environment and effectively avoid malicious behaviors. In order to illustrate the feasibility of this architecture, we design and implement a specific service case named Drone Collision Avoidance Navigation Service based on SDNES. Finally, a simulation experiment for the specific service case and a series of other performance-related experiments were carried out to verify the feasibility and rationality of our proposed architecture. The experimental results demonstrate that SDNES is a promising architecture to assist and accelerate drone flying automation. MDPI 2020-10-30 /pmc/articles/PMC7662669/ /pubmed/33143360 http://dx.doi.org/10.3390/s20216209 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Gao, Ying Liu, Yangliang Wen, Quansi Lin, Hongliang Chen, Yijian Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title | Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title_full | Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title_fullStr | Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title_full_unstemmed | Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title_short | Secure Drone Network Edge Service Architecture Guaranteed by DAG-Based Blockchain for Flying Automation under 5G |
title_sort | secure drone network edge service architecture guaranteed by dag-based blockchain for flying automation under 5g |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662669/ https://www.ncbi.nlm.nih.gov/pubmed/33143360 http://dx.doi.org/10.3390/s20216209 |
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