Open Access

ARTICLE

Prediction of Link Failure in MANET-IoT Using Fuzzy Linear Regression

by R. Mahalakshmi^1,*, V. Prasanna Srinivasan², S. Aghalya³, D. Muthukumaran⁴

1 Department of Electrical and Electronics Engineering, Dhanalakshmi Srinivasan College of Engineering and Technology, Mamallapuram, Tamil Nadu, India
2 Department of Information Technology, R.M.D. Engineering College, Kavaraipettai, Chennai, Tamil Nadu, India
3 Department of Electronics and Communication Engineering, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India
4 Department of Electronics and Communication Engineering, Sri Chandrasekharendra Saraswathi Viswa Mahavidyalaya University, Kancheepuram, Tamil Nadu, India

* Corresponding Author: R. Mahalakshmi. Email:

Intelligent Automation & Soft Computing 2023, 36(2), 1627-1637. https://doi.org/10.32604/iasc.2023.032709

Received 26 May 2022; Accepted 29 June 2022; Issue published 05 January 2023

Abstract

A Mobile Ad-hoc NETwork (MANET) contains numerous mobile nodes, and it forms a structure-less network associated with wireless links. But, the node movement is the key feature of MANETs; hence, the quick action of the nodes guides a link failure. This link failure creates more data packet drops that can cause a long time delay. As a result, measuring accurate link failure time is the key factor in the MANET. This paper presents a Fuzzy Linear Regression Method to measure Link Failure (FLRLF) and provide an optimal route in the MANET-Internet of Things (IoT). This work aims to predict link failure and improve routing efficiency in MANET. The Fuzzy Linear Regression Method (FLRM) measures the long lifespan link based on the link failure. The mobile node group is built by the Received Signal Strength (RSS). The Hill Climbing (HC) method selects the Group Leader (GL) based on node mobility, node degree and node energy. Additionally, it uses a Data Gathering node forward the information from GL to the sink node through multiple GL. The GL is identified by linking lifespan and energy using the Particle Swarm Optimization (PSO) algorithm. The simulation results demonstrate that the FLRLF approach increases the GL lifespan and minimizes the link failure time in the MANET.

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Keywords

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