Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETs Article Swipe
Kudret Yilmaz
,
Resul Kara
,
Ferzan Katircioglu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3552232
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3552232
Mobile 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.
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- OA Status
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- Cited By
- 4
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4408520186Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2025.3552232Digital Object Identifier
- Title
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Energy-Efficient Hybrid Adaptive Clustering for Dynamic MANETsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Kudret Yilmaz, Resul Kara, Ferzan KatirciogluList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2025.3552232Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3552232Direct OA link when available
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Computer science, Cluster analysis, Computer network, Distributed computing, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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31Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.balanced | 250 |
| abstract_inverted_index.cluster. | 82 |
| abstract_inverted_index.compared | 253, 262, 302 |
| abstract_inverted_index.criteria | 120 |
| abstract_inverted_index.critical | 272 |
| abstract_inverted_index.delivery | 282 |
| abstract_inverted_index.division | 57 |
| abstract_inverted_index.ensuring | 247 |
| abstract_inverted_index.example, | 287 |
| abstract_inverted_index.executed | 147 |
| abstract_inverted_index.existing | 255 |
| abstract_inverted_index.learning | 179 |
| abstract_inverted_index.lifetime | 38, 219, 335 |
| abstract_inverted_index.methods. | 180 |
| abstract_inverted_index.mobility | 131 |
| abstract_inverted_index.proposed | 97, 234 |
| abstract_inverted_index.randomly | 16 |
| abstract_inverted_index.selected | 158 |
| abstract_inverted_index.shortens | 35 |
| abstract_inverted_index.Algorithm | 91, 114, 138 |
| abstract_inverted_index.E-MAVMMF, | 266, 305 |
| abstract_inverted_index.Moreover, | 181 |
| abstract_inverted_index.clusters. | 66 |
| abstract_inverted_index.distance, | 126 |
| abstract_inverted_index.effective | 328 |
| abstract_inverted_index.enhancing | 221 |
| abstract_inverted_index.extending | 217 |
| abstract_inverted_index.increases | 27 |
| abstract_inverted_index.maintains | 337 |
| abstract_inverted_index.optimized | 133 |
| abstract_inverted_index.potential | 239 |
| abstract_inverted_index.processes | 226 |
| abstract_inverted_index.remaining | 277, 291 |
| abstract_inverted_index.selecting | 117, 200 |
| abstract_inverted_index.stability | 195 |
| abstract_inverted_index.structure | 26 |
| abstract_inverted_index.two-stage | 87 |
| abstract_inverted_index.wireless, | 7 |
| abstract_inverted_index.Clustering | 90, 113, 166 |
| abstract_inverted_index.algorithm, | 172 |
| abstract_inverted_index.clustering | 46, 145, 225, 256 |
| abstract_inverted_index.efficiency | 215 |
| abstract_inverted_index.end-to-end | 279 |
| abstract_inverted_index.evaluation | 318 |
| abstract_inverted_index.management | 329 |
| abstract_inverted_index.optimizing | 213 |
| abstract_inverted_index.represents | 208 |
| abstract_inverted_index.Performance | 317 |
| abstract_inverted_index.approaches. | 257 |
| abstract_inverted_index.centralized | 20 |
| abstract_inverted_index.conditions. | 232, 343 |
| abstract_inverted_index.consumption | 30, 298 |
| abstract_inverted_index.contributes | 325 |
| abstract_inverted_index.identifying | 149 |
| abstract_inverted_index.management. | 21 |
| abstract_inverted_index.performance | 102, 243, 273, 339 |
| abstract_inverted_index.significant | 210 |
| abstract_inverted_index.throughput. | 285 |
| abstract_inverted_index.Applications | 168 |
| abstract_inverted_index.adaptability | 223 |
| abstract_inverted_index.distribution | 252 |
| abstract_inverted_index.experimental | 288 |
| abstract_inverted_index.improvements | 299 |
| abstract_inverted_index.neighborhood | 125 |
| abstract_inverted_index.transmission | 79 |
| abstract_inverted_index.unsupervised | 178 |
| abstract_inverted_index.inter-cluster | 204 |
| abstract_inverted_index.respectively. | 315 |
| abstract_inverted_index.significantly | 324 |
| abstract_inverted_index.transmission. | 42 |
| abstract_inverted_index.communication. | 205 |
| abstract_inverted_index.(Enhanced-DBSCAN) | 171 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.8799999952316284 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.97837745 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |