Maximizing network efficiency by optimizing channel allocation in wireless body area networks using machine learning techniques

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Rao, V. Chandra Shekhar and Shanmathi, M. and Rajkumar, M. and Abdul Haleem, Sulaimalebbe and Amirthalingam, V. and Vanathi, A. (2025) Maximizing network efficiency by optimizing channel allocation in wireless body area networks using machine learning techniques. Internet Technology Letters, 8 (1). ISSN 24761508

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Abstract

This study proposes a Q-learning algorithm with Adagrad ALR optimizer for channel allocation in Wireless Body Area Networks (WBANs) for IoT healthcare applications. The approach dynamically optimizes channel allocation considering congestion, link quality, and node power, improving network performance, energy efficiency, and battery life. Simulations showed a 17% enhancement in energy efficiency compared to conventional Q-learning, PEH quality-of-service, and clustering algorithms, demonstrating the method's effectiveness for reliable medical data transmission.

Item Type: Article
Additional Information: Cited by: 2; All Open Access; Bronze Open Access
Uncontrolled Keywords: Clustering algorithms; Data communication systems; Data transfer; Electric batteries; Energy efficiency; Energy utilization; Health care; Learning algorithms; Network performance; Reinforcement learning; Channel allocation; Healthcare medical data transmission; Interference; Internet of thing; Machine-learning; Medical data transmission; Optimizers; Q-learning; Wireless body area network; Internet of things
Subjects: Computer Science > Computer Networks and Communications
Divisions: Medicine > Aarupadai Veedu Medical College and Hospital, Puducherry > Pathology
Depositing User: Unnamed user with email techsupport@mosys.org
Date Deposited: 26 Nov 2025 07:14
Last Modified: 26 Nov 2025 07:14
URI: https://vmuir.mosys.org/id/eprint/355

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