Simulation Based Comparative Analysis of Traditional and Hybrid GWO – ML Approaches for Energy Efficient Cluster Head Selection in Dynamic IoT Networks

Simulation Based Comparative Analysis of Traditional and Hybrid GWO – ML Approaches for Energy Efficient Cluster Head Selection in Dynamic IoT Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Simulation Based Comparative Analysis of Traditional and Hybrid GWO – ML Approaches for Energy Efficient Cluster Head Selection in Dynamic IoT Networks Rinki Kaur, Surendra Kumar Patel This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8832975/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Energy efficiency is a crucial issue in the Internet of Thing network due to the sensor nodes limited battery life span. The scalable and sustainable implementation of IoT solution depends on IoT energy efficiency. The changing patterns of energy usage in the IoT environment often exceed the capabilities of traditional metaheuristic algorithms. To address this, we propose a hybrid approach that integrates Grey Wolf Optimization with machine learning for energy efficient Cluster Head selection. The Random Forest-based machine learning model predicts the energy consumption patterns of IoT nodes. Simultaneously, as part of its fitness assessment, GWO uses these predictions to optimize CH placement. The proposed framework was implemented in Python and tested through simulations involving 100 IoT nodes over 50 iterations. Our analysis shows that the proposed model reduces overall energy consumption by approximately 12.44%, enhances load balancing, speeds up convergence, and extends network lifetime compared to traditional GWO. These results indicate that this framework can serve as a scalable solution for sustainable IoT network design and underscore the benefits of combining metaheuristic optimization with predictive intelligence. Internet of Things(IoT) Grey Wolf Optimization Energy Optimization Cluster Head Selection Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 22 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 07 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 05 Apr, 2026 Editor assigned by journal 29 Mar, 2026 Submission checks completed at journal 13 Feb, 2026 First submitted to journal 09 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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