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Routing protocols strategies for flying Ad-Hoc network (FANET): Review, taxonomy, and open research issues

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dc.citedby1
dc.contributor.authorAlmansor M.J.en_US
dc.contributor.authorDin N.M.en_US
dc.contributor.authorBaharuddin M.Z.en_US
dc.contributor.authorMa M.en_US
dc.contributor.authorAlsayednoor H.M.en_US
dc.contributor.authorAl-Shareeda M.A.en_US
dc.contributor.authorAl-asadi A.J.en_US
dc.contributor.authorid59137147200en_US
dc.contributor.authorid9335429400en_US
dc.contributor.authorid35329255600en_US
dc.contributor.authorid7202444897en_US
dc.contributor.authorid59138080200en_US
dc.contributor.authorid57208214655en_US
dc.contributor.authorid59342080800en_US
dc.date.accessioned2025-03-03T07:41:24Z
dc.date.available2025-03-03T07:41:24Z
dc.date.issued2024
dc.description.abstractA Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of applications, including smart ports, delivery of products, construction, monitoring of the environment and climate, and military surveillance. FANETs research is being driven by the potential for UAVs to be utilized in these regions. The purpose of this paper is to provide a comprehensive analysis of the most important FANET characteristics, mobility models, applications, and routing protocols. The present paper is an effort to provide a comprehensive description of the various routing techniques utilized by the most prevalent routing protocols in FANETs, including topology-based, position- based, hierarchical, swarm-based, and Delay Tolerant Networking (DTN) protocols. Reinforcement learning and deep reinforcement learning are both encompassed in a newly anticipated classification. In the meanwhile, this study primarily centres around the taxonomy for learning agents (single- agent, multi-agent) and learning models (model-based and free-model). In addition, the paper intends to shed light on identifying the applications of FANETs in various categories and identify research gaps and future opportunities in this field. In addition, it compares the results qualitatively to those of the previous surveys. Any future work on the FANET routing protocol could benefit from this paper as a reference and roadmap. ? 2024en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.aej.2024.09.032
dc.identifier.epage577
dc.identifier.scopus2-s2.0-85204884646
dc.identifier.spage553
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85204884646&doi=10.1016%2fj.aej.2024.09.032&partnerID=40&md5=9b00c21227544b2ec1bfa081c7053657
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36113
dc.identifier.volume109
dc.pagecount24
dc.publisherElsevier B.V.en_US
dc.sourceScopus
dc.sourcetitleAlexandria Engineering Journal
dc.subjectAdversarial machine learning
dc.subjectAir mobility
dc.subjectDeep learning
dc.subjectDeep reinforcement learning
dc.subjectDelay tolerant networks
dc.subjectEmotional intelligence
dc.subjectHierarchical clustering
dc.subjectMulti agent systems
dc.subjectReinforcement learning
dc.subjectRouting algorithms
dc.subjectUnmanned aerial vehicles (UAV)
dc.subjectVehicular ad hoc networks
dc.subjectAd-hoc networks
dc.subjectAerial vehicle
dc.subjectApplication flying ad-hoc network strategy
dc.subjectFlying ad-hoc network strategy
dc.subjectMobility flying ad-hoc network
dc.subjectNetwork strategy
dc.subjectRouting-protocol
dc.subjectUnmanned aerial vehicle
dc.subjectTaxonomies
dc.titleRouting protocols strategies for flying Ad-Hoc network (FANET): Review, taxonomy, and open research issuesen_US
dc.typeReviewen_US
dspace.entity.typePublication
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