Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETs

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dc.authoridyilmaz, kudret/0000-0003-0996-6090;
dc.contributor.authorYilmaz, Kudret
dc.contributor.authorKara, Resul
dc.contributor.authorKatircioglu, Ferzan
dc.date.accessioned2025-10-11T20:48:15Z
dc.date.available2025-10-11T20:48:15Z
dc.date.issued2025
dc.departmentDüzce Üniversitesien_US
dc.description.abstractMobile ad hoc network (MANET) is a wireless, mobile node network in which the nodes move randomly and operate without centralized management. In MANETs, the network structure increases the energy consumption of the nodes, which shortens the network lifetime and affects packet transmission. The process of clustering in MANETs (Mobile Ad-hoc Network) can be achieved through the division of the network into virtual groups, known as clusters. The Cluster Head (CH) of each cluster is in charge of data transmission within the cluster. In this study, a two-stage Hybrid Adaptive Clustering Algorithm for Dynamics MANETs (HACADM) is proposed to improve the network performance in MANETs. In the first stage, based on the Weighted Clustering Algorithm (WCA) for selecting optimal CHs, criteria such as node degree, neighborhood distance, power of battery and mobility are optimized using the Gravity Search Algorithm (GSA). In the second phase, the clustering is executed by identifying the member nodes and their roles of the selected CHs using the Enhanced Density Based Spatial Clustering of Applications with Noise (Enhanced-DBSCAN) algorithm, which is one of the unsupervised learning methods. Moreover, this approach serves to reduce the load on the CHs and enhance the stability of the cluster by selecting gateway nodes for inter-cluster communication. This study represents a significant step towards optimizing energy efficiency and extending network lifetime by enhancing the adaptability of clustering processes in MANETs under dynamic network conditions. The proposed HACADM method has the potential to enhance the performance of MANETs by ensuring a more balanced load distribution compared to existing clustering approaches. The HACADM method was compared with the EE-WCA, E-MAVMMF, TSDR and MORS-ASO methods using critical performance metrics such as remaining energy, end-to-end delay, packet delivery ratio and throughput. For example, experimental results on remaining energy show that the average energy consumption improvements of HACADM compared with EE-WCA, E-MAVMMF, TSDR and MORS-ASO are 46.38%, 18.35%, 13.08% and 8.33% respectively. Other Performance evaluation results also show that HACADM significantly contributes to the effective management of MANETs, extends the network lifetime and maintains high performance under dynamic network conditions.en_US
dc.identifier.doi10.1109/ACCESS.2025.3552232
dc.identifier.endpage51331en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105001590085en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage51319en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3552232
dc.identifier.urihttps://hdl.handle.net/20.500.12684/21829
dc.identifier.volume13en_US
dc.identifier.wosWOS:001455585800028en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmzKA_WOS_20250911
dc.subjectAd hoc networksen_US
dc.subjectMobile computingen_US
dc.subjectClustering algorithmsen_US
dc.subjectEnergy efficiencyen_US
dc.subjectEnergy consumptionen_US
dc.subjectRoutingen_US
dc.subjectHeuristic algorithmsen_US
dc.subjectRouting protocolsen_US
dc.subjectMobile nodesen_US
dc.subjectParticle swarm optimizationen_US
dc.subjectAdaptive clusteringen_US
dc.subjectenergy efficiencyen_US
dc.subjectgravitational search algorithmen_US
dc.subjectmachine learningen_US
dc.subjectMANETen_US
dc.titleEnergy-Efficient Hybrid Adaptive Clustering for Dynamic MANETsen_US
dc.typeArticleen_US

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