UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review
Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, espe...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Drones |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2504-446X/5/4/148 |
| _version_ | 1797505330423267328 |
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| author | Yassine Yazid Imad Ez-Zazi Antonio Guerrero-González Ahmed El Oualkadi Mounir Arioua |
| author_facet | Yassine Yazid Imad Ez-Zazi Antonio Guerrero-González Ahmed El Oualkadi Mounir Arioua |
| author_sort | Yassine Yazid |
| collection | DOAJ |
| description | Unmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI. |
| first_indexed | 2024-03-10T04:17:05Z |
| format | Article |
| id | doaj.art-7ddd137800ff47be92ca6f68eee36f2b |
| institution | Directory Open Access Journal |
| issn | 2504-446X |
| language | English |
| last_indexed | 2024-03-10T04:17:05Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj.art-7ddd137800ff47be92ca6f68eee36f2b2023-11-23T07:57:47ZengMDPI AGDrones2504-446X2021-12-015414810.3390/drones5040148UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive ReviewYassine Yazid0Imad Ez-Zazi1Antonio Guerrero-González2Ahmed El Oualkadi3Mounir Arioua4Laboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences of Tangier (ENSATg), Abdelmalk Essaadi University, ENSA Tanger, Route Ziaten, Tangier BP 1818, MoroccoNational School of Applied Sciences of Fez (ENSAF), Sidi Mohamed Ben Abdellah University, Fez BP 2626, MoroccoDepartment of Automation, Electrical Engineering and Electronic Technology, Universidad Politécnica de Cartagena, Plaza del Hospital 1, 30202 Cartagena, SpainLaboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences of Tangier (ENSATg), Abdelmalk Essaadi University, ENSA Tanger, Route Ziaten, Tangier BP 1818, MoroccoLaboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences of Tangier (ENSATg), Abdelmalk Essaadi University, ENSA Tanger, Route Ziaten, Tangier BP 1818, MoroccoUnmanned aerial vehicles (UAVs) are becoming integrated into a wide range of modern IoT applications. The growing number of networked IoT devices generates a large amount of data. However, processing and memorizing this massive volume of data at local nodes have been deemed critical challenges, especially when using artificial intelligence (AI) systems to extract and exploit valuable information. In this context, mobile edge computing (MEC) has emerged as a way to bring cloud computing (CC) processes within reach of users, to address computation-intensive offloading and latency issues. This paper provides a comprehensive review of the most relevant research works related to UAV technology applications in terms of enabled or assisted MEC architectures. It details the utility of UAV-enabled MEC architecture regarding emerging IoT applications and the role of both deep learning (DL) and machine learning (ML) in meeting various limitations related to latency, task offloading, energy demand, and security. Furthermore, throughout this article, the reader gains an insight into the future of UAV-enabled MEC, the advantages and the critical challenges to be tackled when using AI.https://www.mdpi.com/2504-446X/5/4/148UAVsIoTcloud computingedge computingMECAI |
| spellingShingle | Yassine Yazid Imad Ez-Zazi Antonio Guerrero-González Ahmed El Oualkadi Mounir Arioua UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review Drones UAVs IoT cloud computing edge computing MEC AI |
| title | UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |
| title_full | UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |
| title_fullStr | UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |
| title_full_unstemmed | UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |
| title_short | UAV-Enabled Mobile Edge-Computing for IoT Based on AI: A Comprehensive Review |
| title_sort | uav enabled mobile edge computing for iot based on ai a comprehensive review |
| topic | UAVs IoT cloud computing edge computing MEC AI |
| url | https://www.mdpi.com/2504-446X/5/4/148 |
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