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AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks.

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Název:	AI-based routing algorithms improve energy efficiency, latency, and data reliability in wireless sensor networks.
Autoři:	Priyadarshi R; Faculty of Engineering and Technology, ITER, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, 751030, India. rahulpriyadarshi@soa.ac.in., Kumar RR; Department of ECE, National Institute of Technology, Patna, Bihar, 800005, India., Ranjan R; School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, 248007, India., Kumar PV; Department of ECE, Aditya University, Surampalem, 533437, India.
Zdroj:	Scientific reports [Sci Rep] 2025 Jul 01; Vol. 15 (1), pp. 22292. Date of Electronic Publication: 2025 Jul 01.
Způsob vydávání:	Journal Article
Jazyk:	English
Informace o časopise:	Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):	Original Publication: London : Nature Publishing Group, copyright 2011-
Abstrakt:	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. (© 2025. The Author(s).)
Competing Interests:	Declarations. Competing interests: The authors declare no competing interests.
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Entry Date(s):	Date Created: 20250702 Latest Revision: 20250706
Update Code:	20250707
PubMed Central ID:	PMC12216685
DOI:	10.1038/s41598-025-08677-w
PMID:	40594790
Databáze:	MEDLINE

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Abstrakt:	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.<br /> (© 2025. The Author(s).)
ISSN:	2045-2322
DOI:	10.1038/s41598-025-08677-w