Smart City Gnosys

Smart city article details

Title	Cluster Head Selection Algorithm Using Machine Learning
ID_Doc	14502
Authors	Nidhya M.S.; Gurumoorthi E.; Babu M.; Charumathi A.C.; Naved M.; Maram B.
Year	2025
Published	4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSADL65848.2025.10933157
Abstract	Wireless Sensor Networks (WSNs) find extensive applications in environmental monitoring, healthcare, and smart cities. Energy efficiency, however, continues to be a significant challenge with the limited lifetime of sensor node batteries. The conventional heuristic-based cluster head (CH) selection techniques tend not to adapt dynamically to network changes, resulting in inefficient energy utilization and decreased network lifetime. This research discusses Machine Learning (ML)-oriented CH selection approaches to improve energy efficiency, network lifetime, and data fusion. Using supervised and unsupervised learning methods, ML algorithms are able to learn optimal CHs dynamically based on residual energy, network structure, and data traffic. The comparative analysis show that ML-based CH selection enhances network stability by 95% and lowers energy consumption by 50% compared to traditional methods. The research points to the promise of ML in WSN performance optimization and opens the doors to intelligent, adaptive clustering technologies. © 2025 IEEE.
Author Keywords	Adaptive Algorithm; Cluster Head Selection; Energy Efficiency; Machine Learning; Network Lifetime; Supervised Learning; Unsupervised Learning; Wireless Sensor Networks (WSN)

Similar Articles

Id	Similarity	Authors	Title	Published
35434	0.921	Srivastava A.; Mishra P.K.	Load-Balanced Cluster Head Selection Enhancing Network Lifetime In Wsn Using Hybrid Approach For Iot Applications	Journal of Sensors, 2023 (2023)
40774	0.919	Kanase S.; Babavali S.F.; Kothapalli S.K.; Thangam A.; Labhade-Kumar N.; Bhoopathy V.	Optimizing Cluster Head Selection In Wireless Sensor Networks Using Mathematical Modeling And Statistical Analysis Of The Hybrid Energy-Efficient Distributed (Heed) Algorithm	Communications on Applied Nonlinear Analysis, 31, 6S (2024)
23874	0.889	Pichamuthu R.; Matheswaran S.; Sengodan P.; Rajkannan K.; Prabhakaran M.	Enhancing Network Lifetime In Iot-Based Wireless Sensor Networks Through Mssa-Driven Cluster Head Optimization	ICDT 2025 - 3rd International Conference on Disruptive Technologies (2025)
989	0.888	Saradha K.R.; Sakthy S.S.	A Comprehensive Survey On Energy-Efficient Wireless Sensor Network Protocols For Real-Time Applications	2025 International Conference on Computing and Communication Technologies, ICCCT 2025 (2025)
6226	0.886	Wang L.; Wang H.	Adaptive Crow Search Algorithm For Hierarchical Clustering In Internet Of Things-Enabled Wireless Sensor Networks	International Journal of Advanced Computer Science and Applications, 16, 4 (2025)
40714	0.884	Sambo D.W.; Yenke B.O.; Förster A.; Dayang P.	Optimized Clustering Algorithms For Large Wireless Sensor Networks: A Review	Sensors (Switzerland), 19, 2 (2019)
913	0.884	Nedham W.B.; Al-Qurabat A.K.M.	A Comprehensive Review Of Clustering Approaches For Energy Efficiency In Wireless Sensor Networks	International Journal of Computer Applications in Technology, 72, 2 (2023)
14503	0.883	Jesi P.M.; Antony Asir Daniel V.; Rajagopal R.; Femila L.	Cluster Head Selection Using Multi-Dilation Convolutional Neural Network Optimized With Bcmo For Iot Networks	IETE Journal of Research, 70, 8 (2024)
29744	0.883	Aleem A.; Thumma R.	Hybrid Energy-Efficient Clustering With Reinforcement Learning For Iot-Wsns Using Knapsack And K-Means	IEEE Sensors Journal (2025)
8457	0.879	Baskaran P.; Karuppasamy K.	An Integrated Model For Energy Conservation In Iot-Enabled Wsn Using Adaptive Regional Clustering And Monkey Inspired Optimization	Journal of Intelligent and Fuzzy Systems, 43, 4 (2022)