Machine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issues

dc.authoridHeidari, Arash/0000-0003-4279-8551
dc.authoridJafari Navimipour, Nima/0000-0002-5514-5536
dc.authorwosidHeidari, Arash/AAK-9761-2021
dc.authorwosidJafari Navimipour, Nima/AAF-5662-2021
dc.contributor.authorHeidari, Arash
dc.contributor.authorNavimipour, Nima Jafari
dc.contributor.authorUnal, Mehmet
dc.contributor.authorZhang, Guodao
dc.date.accessioned2023-10-19T15:11:34Z
dc.date.available2023-10-19T15:11:34Z
dc.date.issued2023
dc.department-temp[Heidari, Arash] Islamic Azad Univ, Dept Comp Engn, Tabriz Branch, Tabriz, Iran; [Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, TR-34083 Istanbul, Turkiye; [Unal, Mehmet] Nisantasi Univ, Dept Comp Engn, Istanbul, Turkiye; [Zhang, Guodao] Hangzhou Dianzi Univ, Dept Digital Media Technol, Hangzhou 310018, Peoples R China; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu 64002, Yunlin, Taiwanen_US
dc.description.abstractDeep Learning (DL) and Machine Learning (ML) are effectively utilized in various complicated challenges in healthcare, industry, and academia. The Internet of Drones (IoD) has lately cropped up due to high adjustability to a broad range of unpredictable circumstances. In addition, Unmanned Aerial Vehicles ( UAVs) could be utilized efficiently in a multitude of scenarios, including rescue missions and search, farming, mission-critical services, surveillance systems, and so on, owing to technical and realistic benefits such as low movement, the capacity to lengthen wireless coverage zones, and the ability to attain places unreachable to human beings. In many studies, IoD and UAV are utilized interchangeably. Besides, drones enhance the efficiency aspects of various network topologies, including delay, throughput, interconnectivity, and dependability. Nonetheless, the deployment of drone systems raises various challenges relating to the inherent unpredictability of the wireless medium, the high mobility degrees, and the battery life that could result in rapid topological changes. In this paper, the IoD is originally explained in terms of potential applications and comparative operational scenarios. Then, we classify ML in the IoD-UAV world according to its applications, including resource management, surveillance and monitoring, object detection, power control, energy management, mobility management, and security management. This research aims to supply the readers with a better understanding of (1) the fundamentals of IoD/UAV, (2) the most recent developments and breakthroughs in this field, (3) the benefits and drawbacks of existing methods, and (4) areas that need further investigation and consideration. The results suggest that the Convolutional Neural Networks (CNN) method is the most often employed ML method in publications. According to research, most papers are on resource and mobility management. Most articles have focused on enhancing only one parameter, with the accuracy parameter receiving the most attention. Also, Python is the most commonly used language in papers, accounting for 90% of the time. Also, in 2021, it has the most papers published.en_US
dc.identifier.citation35
dc.identifier.doi10.1145/3571728en_US
dc.identifier.issn0360-0300
dc.identifier.issn1557-7341
dc.identifier.issue12en_US
dc.identifier.scopus2-s2.0-85146781050en_US
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1145/3571728
dc.identifier.urihttps://hdl.handle.net/20.500.12469/5090
dc.identifier.volume55en_US
dc.identifier.wosWOS:000952547400007en_US
dc.identifier.wosqualityQ1
dc.khas20231019-WoSen_US
dc.language.isoenen_US
dc.publisherAssoc Computing Machineryen_US
dc.relation.ispartofAcm Computing Surveysen_US
dc.relation.publicationcategoryDiğeren_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectObject DetectionEn_Us
dc.subjectSmart CitiesEn_Us
dc.subjectUav NetworksEn_Us
dc.subjectDeepEn_Us
dc.subjectManagementEn_Us
dc.subjectPowerEn_Us
dc.subjectIdentificationEn_Us
dc.subjectInterferenceEn_Us
dc.subjectOptimizationEn_Us
dc.subjectChallengesEn_Us
dc.subjectObject Detection
dc.subjectSmart Cities
dc.subjectUav Networks
dc.subjectDeep
dc.subjectManagement
dc.subjectInternet of Dronesen_US
dc.subjectPower
dc.subjectIoDen_US
dc.subjectIdentification
dc.subjectreviewen_US
dc.subjectInterference
dc.subjectUAVen_US
dc.subjectOptimization
dc.subjectMachine Learningen_US
dc.subjectChallenges
dc.subjectDeep Learningen_US
dc.titleMachine Learning Applications in Internet-of-Drones: Systematic Review, Recent Deployments, and Open Issuesen_US
dc.typeReviewen_US
dspace.entity.typePublication

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