ItemConference Paper

Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System

Title
Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System
Creator
Srivastava, Shubhi; Gupta, Roopesh; Banerjee, Somnath; Vijayan, Alpha; Thacker, Chintan; Vasmatkar, Abhijit
Description
Healthcare IoT systems have been plagued with significant challenges with regard to maintaining an optimum QoS due to the dynamic conditions of the network, diverse device capabilities, and stringent real-time constraints imposed by patient monitoring-type applications. Traditional QoS mechanisms are basically static; they do not take into account changes within the network. Hence, service delivery experiences degradation, with attendant risk to patients' safety. As a solution, this research proposes an adaptive QoS approach employing Deep Q-Network (DQN) reinforcement learning algorithms to dynamically control resource allocation and traffic prioritization in healthcare IoT networks. This system involves multi-agent reinforcement learning architecture where continuous state-action space mapping is utilized for adjusting bandwidth allocation, latency management, and packet prioritization automatically based on network conditions and the criticality levels of applications in real-time. Experimentally, the solution has attained an accuracy of 94.7 percent in QoS prediction, an 87.3 percent reduction in average latency to critical healthcare applications, 91.2 percent improvement in network throughput utilization, and an 89.6 percent success rate in adhering to service level agreements in peak traffic conditions. Through reinforcement learning-based decision making, the adaptive QoS mechanism dynamically accommodates the requirements of healthcare IoT, ensuring reliable service delivery while optimizing the usage of network resources. 2025 IEEE.
Source
2nd International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
adaptive systems; healthcare IOT; network optimization; quality of service; reinforcement learning
Coverage
Srivastava S., New Horizon College of Engineering, Department of Information Science and Engineering, Bengaluru, India; Gupta R., SCES's Indira School of Business Studies, Department of Marketing, Pune, India; Banerjee S., Sun Prairie, United States; Vijayan A., Christ University, Department of Artificial Intelligence Machine Learning & Data Science (AIML&DS), Bengaluru, India; Thacker C., Parul institute of Engineering and Technology, Parul University, Faculty of Engineering and Technology, Department of Computer science and Engineering, P.O.Limda, India; Vasmatkar A., Symbiosis Law School, Symbiosis International (Deemed University), Faculty of Law, Pune, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833153677-0;
Format
online
Language
English
Type
Conference paper

Citation

Srivastava, Shubhi; Gupta, Roopesh; Banerjee, Somnath; Vijayan, Alpha; Thacker, Chintan; Vasmatkar, Abhijit, “Prioritized QoS Enforcement in Smart Healthcare IoT Using Adaptive Deep Q-Network-Based Traffic Decision System,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25852.

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