Improved K-means clustering and adaptive distance threshold for energy reduction in WSN-IoTs
The Internet of Things (IoTs) increasingly depends on Wireless Sensor Networks (WSNs) for real time data collection and communication. However, due to the limited battery capacity of sensor nodes, energy efficiency remains a critical challenge, especially since data transmission consumes the most en...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier B.V.
2025
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/44972/ http://umpir.ump.edu.my/id/eprint/44972/1/Improved%20K-means%20clustering%20and%20adaptive%20distance%20threshold.pdf |
| Summary: | The Internet of Things (IoTs) increasingly depends on Wireless Sensor Networks (WSNs) for real time data collection and communication. However, due to the limited battery capacity of sensor nodes, energy efficiency remains a critical challenge, especially since data transmission consumes the most energy. This study introduces an enhanced energy aware clustering approach that combines an improved K-Means algorithm with an adaptive distance threshold to optimize relay node selection and cluster formation. The method considers node proximity, residual energy, and overall network conditions to achieve balanced energy distribution across the network. The proposed approach was evaluated against established protocols including Hybrid Energy-Efficient Distributed Clustering (HEED), Threshold-Sensitive Energy-Efficient Sensor Network (TEEN), and previous versions of the Energy Efficient Cluster and Routing (EECR) protocol under three different deployment scenarios. Experimental results show that the enhanced EECR protocol reduces energy consumption by 5 % and significantly extends network lifetime, outperforming conventional techniques. The inclusion of adaptive distance thresholds proves effective in minimizing unnecessary energy drain and improving the reliability of data transmission. These results highlight the method's potential as a scalable and energy efficient solution for future IoT applications involving large scale sensor networks. |
|---|