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Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

Mahendrakumar Subramaniam1,*, V. Vedanarayanan2, Azath Mubarakali3, S. Sathiya Priya4

1 Department of Electronics and Communications Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
2 Department of Electronics and Communication Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India
3 College of Computer Science, King Khalid University, Abha, Saudi Arabia
4 Department of Electronics and Communications Engineering, Panimalar Institute of Technology, Chennai, Tamil Nadu, India

* Corresponding Author: Mahendrakumar Subramaniam. Email: email

Intelligent Automation & Soft Computing 2023, 36(2), 1603-1612. https://doi.org/10.32604/iasc.2023.032396

Abstract

Machine Learning concepts have raised executions in all knowledge domains, including the Internet of Thing (IoT) and several business domains. Quality of Service (QoS) has become an important problem in IoT surrounding since there is a vast explosion of connecting sensors, information and usage. Sensor data gathering is an efficient solution to collect information from spatially disseminated IoT nodes. Reinforcement Learning Mechanism to improve the QoS (RLMQ) and use a Mobile Sink (MS) to minimize the delay in the wireless IoT s proposed in this paper. Here, we use machine learning concepts like Reinforcement Learning (RL) to improve the QoS and energy efficiency in the Wireless Sensor Network (WSN). The MS collects the data from the Cluster Head (CH), and the RL incentive values select CH. The incentives value is computed by the QoS parameters such as minimum energy utilization, minimum bandwidth utilization, minimum hop count, and minimum time delay. The MS is used to collect the data from CH, thus minimizing the network delay. The sleep and awake scheduling is used for minimizing the CH dead in the WSN. This work is simulated, and the results show that the RLMQ scheme performs better than the baseline protocol. Results prove that RLMQ increased the residual energy, throughput and minimized the network delay in the WSN.

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APA Style
Subramaniam, M., Vedanarayanan, V., Mubarakali, A., Priya, S.S. (2023). Reinforcement learning to improve qos and minimizing delay in iot. Intelligent Automation & Soft Computing, 36(2), 1603-1612. https://doi.org/10.32604/iasc.2023.032396
Vancouver Style
Subramaniam M, Vedanarayanan V, Mubarakali A, Priya SS. Reinforcement learning to improve qos and minimizing delay in iot. Intell Automat Soft Comput . 2023;36(2):1603-1612 https://doi.org/10.32604/iasc.2023.032396
IEEE Style
M. Subramaniam, V. Vedanarayanan, A. Mubarakali, and S.S. Priya "Reinforcement Learning to Improve QoS and Minimizing Delay in IoT," Intell. Automat. Soft Comput. , vol. 36, no. 2, pp. 1603-1612. 2023. https://doi.org/10.32604/iasc.2023.032396



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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