Omkar Singh
ABSTRACT
A sensor node (SN) in Wireless Sensor Networks (WSN) has a limited amount of energy outfitted with a battery. Generally, sensors are positioned in such an environment wherever it is typical to reach the sensors and change or recharge them. QoS is critical in prolonging the network lifetime and attaining better throughput for a large network in WSN. Hence, a practical protocol is required that improves QoS and enhances the lifetime of the network. In this paper, QoS Learning Approach (QoS-LA) using Reinforcement Learning is developed and assessed. The proposed routing approach prolongs network lifetime by electing the optimum route to send data. The best route is selected as per extreme residual power at SN for a next-hop with excellence link and delay with error rate. The proposed approach is implemented on MATLAB on various SNs 100--1000.to judge the effectiveness on multiple parameters and assessed with state-of-the-art algorithms CNN, QoS-R, and KNN. The Simulation outcomes reveal that the QoS-LA approach offers the better results in terms of energy consumption reduces 14%-16%, number of alive nodes enhances 18%-20% that provides the network lifetime, and enhances the throughput to send data to base station 9%-11%.
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Index Terms
- QoS-Attentive Learning-based Routing for Scalable WSNs
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