Open Access

ARTICLE

Reinforcement Learning to Improve QoS and Minimizing Delay in IoT

by Mahendrakumar Subramaniam^1,*, 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:

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

Received 16 May 2022; Accepted 06 July 2022; Issue published 05 January 2023

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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Keywords

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