Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering
Wireless sensor networks (WSNs) play a vital role in modern applications such as healthcare, smart cities, and environmental monitoring. However, their potential is often limited by energy constraints, which reduce the lifetime of the network and the data collection capabilities. To address this cha...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11086577/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850071620458119168 |
|---|---|
| author | Thierry Taning Longla Tansal Gucluoglu Tamer Dag |
| author_facet | Thierry Taning Longla Tansal Gucluoglu Tamer Dag |
| author_sort | Thierry Taning Longla |
| collection | DOAJ |
| description | Wireless sensor networks (WSNs) play a vital role in modern applications such as healthcare, smart cities, and environmental monitoring. However, their potential is often limited by energy constraints, which reduce the lifetime of the network and the data collection capabilities. To address this challenge, we propose KDL, a novel hybrid clustering algorithm that combines K-Nearest Neighbor (KNN) and Density-Based Spatial Clustering (DBSCAN) to optimize energy efficiency in WSNs. KDL first uses KNN to analyze internode distances and determine optimal clustering parameters, which guide DBSCAN in forming robust clusters. After clustering, KNN reassigns noise points to appropriate clusters, improving coverage. Next, an energy-aware cluster head (CH) selection mechanism, inspired by LEACH but enhanced with node energy levels and cluster centroid distances, ensures balanced energy consumption. In addition, a relay-assisted communication strategy optimizes data transmission by strategically placing relay nodes between the CH and the base station. Through extensive simulations, KDL shows significant performance improvements over existing approaches, substantially enhancing network lifetime while maintaining energy efficiency. These advancements position KDL as a promising solution for real-world energy-efficient WSN deployments. |
| format | Article |
| id | doaj-art-b453bf56abf94e9c8c765d296346aee0 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-b453bf56abf94e9c8c765d296346aee02025-08-20T02:47:15ZengIEEEIEEE Access2169-35362025-01-011312786812788410.1109/ACCESS.2025.359079011086577Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static ClusteringThierry Taning Longla0https://orcid.org/0009-0006-9159-735XTansal Gucluoglu1https://orcid.org/0000-0002-4090-005XTamer Dag2Electronics and Communications Engineering Department, Yildiz Technical University, Istanbul, TürkiyeElectronics and Communications Engineering Department, Yildiz Technical University, Istanbul, TürkiyeCollege of Engineering and Technology, American University of the Middle East, Egaila, KuwaitWireless sensor networks (WSNs) play a vital role in modern applications such as healthcare, smart cities, and environmental monitoring. However, their potential is often limited by energy constraints, which reduce the lifetime of the network and the data collection capabilities. To address this challenge, we propose KDL, a novel hybrid clustering algorithm that combines K-Nearest Neighbor (KNN) and Density-Based Spatial Clustering (DBSCAN) to optimize energy efficiency in WSNs. KDL first uses KNN to analyze internode distances and determine optimal clustering parameters, which guide DBSCAN in forming robust clusters. After clustering, KNN reassigns noise points to appropriate clusters, improving coverage. Next, an energy-aware cluster head (CH) selection mechanism, inspired by LEACH but enhanced with node energy levels and cluster centroid distances, ensures balanced energy consumption. In addition, a relay-assisted communication strategy optimizes data transmission by strategically placing relay nodes between the CH and the base station. Through extensive simulations, KDL shows significant performance improvements over existing approaches, substantially enhancing network lifetime while maintaining energy efficiency. These advancements position KDL as a promising solution for real-world energy-efficient WSN deployments.https://ieeexplore.ieee.org/document/11086577/Wireless sensor networkdensity-based spatial clustering (DBSCAN) with noise applicationK-nearest neighbor (KNN)low-energy adaptive clustering hierarchy (LEACH)relay nodes |
| spellingShingle | Thierry Taning Longla Tansal Gucluoglu Tamer Dag Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering IEEE Access Wireless sensor network density-based spatial clustering (DBSCAN) with noise application K-nearest neighbor (KNN) low-energy adaptive clustering hierarchy (LEACH) relay nodes |
| title | Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering |
| title_full | Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering |
| title_fullStr | Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering |
| title_full_unstemmed | Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering |
| title_short | Relay-Assisted Wireless Sensor Networks Using KNN-DBSCAN Static Clustering |
| title_sort | relay assisted wireless sensor networks using knn dbscan static clustering |
| topic | Wireless sensor network density-based spatial clustering (DBSCAN) with noise application K-nearest neighbor (KNN) low-energy adaptive clustering hierarchy (LEACH) relay nodes |
| url | https://ieeexplore.ieee.org/document/11086577/ |
| work_keys_str_mv | AT thierrytaninglongla relayassistedwirelesssensornetworksusingknndbscanstaticclustering AT tansalgucluoglu relayassistedwirelesssensornetworksusingknndbscanstaticclustering AT tamerdag relayassistedwirelesssensornetworksusingknndbscanstaticclustering |