A Novel Manet Based on Fuzzy's Extreme Machine Learning

R.Surendiran

doi:https://doi.org/10.59232/MS-V1I1P103

Research Article | Open Access | Download Full Text

Volume 1 | Issue 1 | Year 2023 | Article Id: MS-V1I1P103 DOI: https://doi.org/10.59232/MS-V1I1P103

A Novel Manet Based on Fuzzy's Extreme Machine Learning

R.Surendiran

Received	Revised	Accepted	Published
30 Jun 2023	25 Jul 2023	31 Aug 2023	03 Oct 2023

Citation

R.Surendiran. “A Novel Manet Based on Fuzzy's Extreme Machine Learning.” DS Journal of Modeling and Simulation, vol. 1, no. 1, pp. 25-32, 2023.

Abstract

A wireless network with several peer nodes is known as a Mobile Ad-hoc Network. (MANET). One of the largest barriers to growing multicast routing protocols in ad-hoc networks is the confined battery existence of cell nodes. The multicast routing technology can dramatically improve the MANET network's availability. This protocol's goal is to shorten the MANET network lifetime and energy usage. A novel, Manet-based ultra-fuzzy machine learning (MFE-ML) approach has been put forth in this study to solve these problems. Two phases make up the whole process like, Cluster Head (CH) selection and routing. Fuzzy ELM is used in the first phase to choose the CHs. Monte Carlo simulation is used in the second phase to execute routing depending on variables such as residual energy, node order, node distance, and CH order. The effectiveness of the proposed MFE-ML technique is evaluated using a number of metrics, such as community lifetime, common cease-to-cease delay, packet transit speed, throughput, and standardization data use on energy. The result of simulations shows that MFE-ML is an efficient solution for routing in the networks. Compared to GKCA, RRCST, and EBCH, the MFE-ML technique extended the network lifetime by 17.24%, 19.45%, and 22.34%, respectively.

Keywords

MANET, Clustering, Cluster head selection, Monte-Carlo simulation, Fuzzy extreme learning machine.

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