Enhancing Quality of Service in Cluster-Based IoT Routing Using Optimized Lion Routing with Deep Deterministic Policy Gradient (OLRDDPG)

Enhancing Quality of Service in Cluster-Based IoT Routing Using Optimized Lion Routing with Deep Deterministic Policy Gradient (OLRDDPG) | 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 Enhancing Quality of Service in Cluster-Based IoT Routing Using Optimized Lion Routing with Deep Deterministic Policy Gradient (OLRDDPG) Yanitha Ramasamy, Logambal M This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8570903/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The combination of limited energy resources, changeable network topology, and extensive heterogeneous deployments, the quick expansion of Internet of Things (IoT) networks has created major issues in guaranteeing Quality of Service (QoS). Reducing latency, optimizing data delivery, and extending network lifetime all depend on effective routing. Conventional routing protocols and independent metaheuristic optimization methods frequently have poor performance in dynamic environments and little adaptability. This research suggests Optimized Lion Routing with Deep Deterministic Policy Gradient (OLRDDPG), a unique hybrid routing architecture for cluster-based IoT networks, as a solution to these problems. To accomplish adaptive and QoS-aware routing, the suggested method combines the continuous decision-making power of Deep Deterministic Policy Gradient (DDPG) with the global exploration capabilities of the Lion Optimization Algorithm (LOA). In the suggested model, DDPG dynamically optimizes routing choices based on network conditions including residual energy, delay, and connection quality, whereas LOA is used for effective route discovery and population initialization. Key QoS parameters, including as End-to-End Delay, Packet Delivery Ratio (PDR), Routing Overhead, Throughput, and Energy Consumption, are used to assess OLRDDPG's routing performance. The suggested OLRDDPG greatly outperforms current methods like Glowworm Swarm Optimization (GSO), Shuffled Frog Leaping Algorithm (SFLA), Convolutional Lion Routing Optimization (CLRO), and Deep Belief Lion Optimization (DBLO), according to extensive simulation findings. The findings demonstrate that OLRDDPG is a good fit for large-scale, dynamic IoT applications because it reduces latency and energy consumption while increasing network throughput and dependability. Internet of Things (IoT) Cluster-Based Routing Lion Optimization Algorithm Deep Deterministic Policy Gradient Quality of Service Energy-Efficient Routing Deep Reinforcement Learning Hybrid Optimization Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8570903","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":572637193,"identity":"e9e6ca57-48e1-47be-831f-e9100a405eb8","order_by":0,"name":"Yanitha 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