Energy-Balanced Hierarchical Routing for Monitoring and Industrial IoT Applications in Dense Wireless Sensor Networks

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Abstract

Abstract The quick growth of large-scale Internet of Things (IoT) deployments has made it even more important for high-density wireless sensor networks (WSNs) to have stable and energy-efficient routing mechanisms. Traditional hierarchical routing protocols use linear, probability-based methods to choose cluster heads (CHs). These methods often cause random energy imbalance and early network fragmentation. This paper suggests an Advanced Quantum-Adaptive Hierarchical Routing (A-QAHR) protocol that uses a quantum-inspired stochastic tunneling (QST) mechanism for non-linear CH selection to get around these issues. The proposed model sees the energy left in a node as a dynamic state variable and uses quantum barrier logic to move CH duties away from nodes that need energy. A new cross-layer entanglement factor considers residual energy, node density, and distance to the sink to make sure that energy is used up evenly and routing is more stable. The proposed protocol was evaluated using Monte Carlo simulations implemented in MATLAB R2021a and comparing its results to those of state-of-the-art protocols like LEACH, SEP, and DEEC. The simulation results show that A-QAHR increases the stability period (the first node to die) by 30.5% compared to DEEC and by 67.7% compared to LEACH. The proposed protocol also lowers the average energy use per packet to 1.95 mJ and raises the total amount of data sent to the base station by 40.6%. A statistical analysis shows that the improvements are statistically significant (p < 0.005). Finally, A-QAHR protocol is a scalable and energy-balanced routing solution that is perfect for next-generation massive IoT applications that need a long network lifetime and high reliability.
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Energy-Balanced Hierarchical Routing for Monitoring and Industrial IoT Applications in Dense Wireless Sensor 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 Energy-Balanced Hierarchical Routing for Monitoring and Industrial IoT Applications in Dense Wireless Sensor Networks Sunil S Harakannanavar, Sapnakumari C, Veena I Puranikmath, Ramesh B Koti, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8676564/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 18 You are reading this latest preprint version Abstract The quick growth of large-scale Internet of Things (IoT) deployments has made it even more important for high-density wireless sensor networks (WSNs) to have stable and energy-efficient routing mechanisms. Traditional hierarchical routing protocols use linear, probability-based methods to choose cluster heads (CHs). These methods often cause random energy imbalance and early network fragmentation. This paper suggests an Advanced Quantum-Adaptive Hierarchical Routing (A-QAHR) protocol that uses a quantum-inspired stochastic tunneling (QST) mechanism for non-linear CH selection to get around these issues. The proposed model sees the energy left in a node as a dynamic state variable and uses quantum barrier logic to move CH duties away from nodes that need energy. A new cross-layer entanglement factor considers residual energy, node density, and distance to the sink to make sure that energy is used up evenly and routing is more stable. The proposed protocol was evaluated using Monte Carlo simulations implemented in MATLAB R2021a and comparing its results to those of state-of-the-art protocols like LEACH, SEP, and DEEC. The simulation results show that A-QAHR increases the stability period (the first node to die) by 30.5% compared to DEEC and by 67.7% compared to LEACH. The proposed protocol also lowers the average energy use per packet to 1.95 mJ and raises the total amount of data sent to the base station by 40.6%. A statistical analysis shows that the improvements are statistically significant (p < 0.005). Finally, A-QAHR protocol is a scalable and energy-balanced routing solution that is perfect for next-generation massive IoT applications that need a long network lifetime and high reliability. Wireless sensor networks Energy-efficient routing Internet of Things Hierarchical clustering Quantum-inspired optimization. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers agreed at journal 05 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 25 Feb, 2026 Submission checks completed at journal 27 Jan, 2026 First submitted to journal 23 Jan, 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. 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