Mobility and Resource Allocation with Intelligent Clustering in UAVs Assisted VANETs

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dc.contributor.authorMahmood, S.N.
dc.contributor.authorAl-Dolaimy, F.
dc.contributor.authorAlkhayyat, A.
dc.contributor.authorAlani, S.
dc.contributor.authorAlkhafaji, M.A.
dc.contributor.authorAbbas, F.H.
dc.contributor.authorGuneser, M.T.
dc.date.accessioned2024-09-29T16:21:02Z
dc.date.available2024-09-29T16:21:02Z
dc.date.issued2023
dc.departmentKarabük Üniversitesien_US
dc.description2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023 -- 9 June 2023 through 11 June 2023 -- Bhubaneswar -- 191494en_US
dc.description.abstractIn the past decade, there has been significant development and utilization of Unmanned Aerial Vehicles (UAVs) in Vehicular Ad Hoc Networks (VANETs) across various applications. The integration of UAVs in VANETs has provided vehicles with enhanced performance by enabling communication through an aerial medium, thereby bypassing ground-level obstacles. Efficient management of mobility and network resources becomes crucial when dealing with a large number of highly dynamic vehicles. To address this, the proposed approach in this paper is Mobility and Resource Allocation with Intelligent Clustering in UAVs-assisted VANETs (MRAIC-UAVs). The key components of the proposed approach include the network model, mobility model, clustering strategy, and UAVs Cluster Head (CH) selection process. The selection of CHs is based on the evaluation of parameters such as residual energy, UAVs mobility, UAVs degree difference, distance, and UAVs stability. This approach significantly improves network energy efficiency and packet delivery ratio. The simulation is conducted using OMNET++ with the SUMO mobility generator, and a comparison is made with earlier models such as PRO-UAVs and RJEDC-UAVs. The performance analysis considers parameters such as packet delivery ratio, end-to-end delay, energy efficiency, and energy consumption. The simulation results demonstrate that the proposed MRAIC-UAVs approach achieves higher energy efficiency and packet delivery ratio while exhibiting lower end-to-end delay and energy consumption compared to earlier approaches. © 2023 IEEE.en_US
dc.identifier.doi10.1109/APSIT58554.2023.10201668
dc.identifier.endpage571en_US
dc.identifier.isbn979-835033936-9
dc.identifier.scopus2-s2.0-85168713117en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage566en_US
dc.identifier.urihttps://doi.org/10.1109/APSIT58554.2023.10201668
dc.identifier.urihttps://hdl.handle.net/20.500.14619/9482
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartof2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectassisted vehicular adhoc networken_US
dc.subjectIntelligent Clusteringen_US
dc.subjectunmanned aerials vehiclesen_US
dc.titleMobility and Resource Allocation with Intelligent Clustering in UAVs Assisted VANETsen_US
dc.typeConference Objecten_US

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