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QoS-Attentive Learning-based Routing for Scalable WSNs

Published: 13 January 2022 Publication History

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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  • (2023)Machine Learning Based Techniques for Node Localization in WSN: A Survey2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT)10.1109/DICCT56244.2023.10110235(12-17)Online publication date: 17-Mar-2023

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            cover image ACM Other conferences
            DSMLAI '21': Proceedings of the International Conference on Data Science, Machine Learning and Artificial Intelligence
            August 2021
            415 pages
            ISBN:9781450387637
            DOI:10.1145/3484824
            Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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            Published: 13 January 2022

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            Author Tags

            1. CNN
            2. Clustering
            3. Cost Function
            4. KNN and QoS-LA
            5. QoS-R
            6. Reinforcement Learning
            7. Routing
            8. WSN

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            • (2023)Machine Learning Based Techniques for Node Localization in WSN: A Survey2023 International Conference on Device Intelligence, Computing and Communication Technologies, (DICCT)10.1109/DICCT56244.2023.10110235(12-17)Online publication date: 17-Mar-2023

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