AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks
Abstract This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). U...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-08677-w |
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| _version_ | 1849402641133600768 |
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| author | Rahul Priyadarshi Ravi Ranjan Kumar Rakesh Ranjan Padarti Vijaya Kumar |
| author_facet | Rahul Priyadarshi Ravi Ranjan Kumar Rakesh Ranjan Padarti Vijaya Kumar |
| author_sort | Rahul Priyadarshi |
| collection | DOAJ |
| description | Abstract This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). Unlike conventional approaches that rely on static or standalone algorithms, the proposed framework employs a structured decision-making pipeline that dynamically adapts to real-time changes in network topology, traffic, and energy conditions. Each AI module plays a distinct role-RL handles local routing decisions, while GA and PSO are invoked for global optimization under resource constraints. Simulations conducted in MATLAB R2021b validate the framework’s effectiveness, demonstrating improvements in packet delivery ratio, end-to-end latency, and energy efficiency when compared to traditional protocols. While this study is based on synthetic evaluations, it outlines the architectural groundwork for future real-world implementation and discusses deployment challenges such as scalability, resource usage, and security. The results highlight the potential of hybrid AI-based routing strategies to enhance the reliability, adaptability, and sustainability of WSNs in dynamic and resource-limited environments. |
| format | Article |
| id | doaj-art-3c5c11fc20c84f5e9fd502db49d58d49 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-3c5c11fc20c84f5e9fd502db49d58d492025-08-20T03:37:30ZengNature PortfolioScientific Reports2045-23222025-07-0115111910.1038/s41598-025-08677-wAI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networksRahul Priyadarshi0Ravi Ranjan Kumar1Rakesh Ranjan2Padarti Vijaya Kumar3Faculty of Engineering and Technology, ITER, Siksha ‘O’ Anusandhan (Deemed to be University)Department of ECE, National Institute of TechnologySchool of Computer Science, University of Petroleum and Energy StudiesDepartment of ECE, Aditya UniversityAbstract This paper proposes a modular Artificial Intelligence (AI)-based routing framework for Wireless Sensor Networks (WSNs) that integrates reinforcement learning (RL), supervised learning, and swarm intelligence techniques such as genetic algorithms (GA) and particle swarm optimization (PSO). Unlike conventional approaches that rely on static or standalone algorithms, the proposed framework employs a structured decision-making pipeline that dynamically adapts to real-time changes in network topology, traffic, and energy conditions. Each AI module plays a distinct role-RL handles local routing decisions, while GA and PSO are invoked for global optimization under resource constraints. Simulations conducted in MATLAB R2021b validate the framework’s effectiveness, demonstrating improvements in packet delivery ratio, end-to-end latency, and energy efficiency when compared to traditional protocols. While this study is based on synthetic evaluations, it outlines the architectural groundwork for future real-world implementation and discusses deployment challenges such as scalability, resource usage, and security. The results highlight the potential of hybrid AI-based routing strategies to enhance the reliability, adaptability, and sustainability of WSNs in dynamic and resource-limited environments.https://doi.org/10.1038/s41598-025-08677-w |
| spellingShingle | Rahul Priyadarshi Ravi Ranjan Kumar Rakesh Ranjan Padarti Vijaya Kumar AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks Scientific Reports |
| title | AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks |
| title_full | AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks |
| title_fullStr | AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks |
| title_full_unstemmed | AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks |
| title_short | AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks |
| title_sort | ai based routing algorithms improve energy efficiency latency and data reliability in wireless sensor networks |
| url | https://doi.org/10.1038/s41598-025-08677-w |
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