Multi-Objective Heterogeneous Clustering Approach for Efficient-Energy Optimization in WSN

Prajapati, Abhishek; Kumar, Vinay; Rajpoot, Prince; Dubey, Kumkum; Singh, Shubham; Verma, Neetu

doi:10.1109/confluence.2019.8776965

Cited by 3 publications

(1 citation statement)

References 17 publications

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“…All CHs broadcast the message of being a CH and all normal nodes select the nearest CH based on the signal strength using join message. This phase again reduces the energy consumption and guarantees the lower distance between nodes and respective CH [4].…”

Section: Et (Chi−bs)=transmission Energy To Transfer Data From Chi To Bsmentioning

confidence: 99%

Energy Efficient Heterogeneous WNS Clustering Using Machine Learning

Maheshwar¹,

Veenadhari²,

Almelu³

2021

IJOSCIENCE

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Heterogeneous wireless sensor network (HWSN) fulfills the requirements of researchers in the design of real-life application to resolve the issues of unattended problem. Wireless sensor networks are used in diverse areas such as battlefields, security, hospitals, universities, etc. It has been used in our everyday lives. Its development is rising day by day. Wireless sensor network includes hundreds to thousands of sensor nodes which aid in gathering various information like temperature, sound, location, etc. Recharging or modifying sensor nodes which might have limited battery power is usually difficult. Therefore, energy conservation is a crucial concern in sustaining the network. Clustering the networks is definitely one of the most common solutions for rendering WSNs energy. In this paper, review and compare different energy-efficient clustering protocols for WSNs

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Section: Et (Chi−bs)=transmission Energy To Transfer Data From Chi To Bsmentioning

confidence: 99%

Energy Efficient Heterogeneous WNS Clustering Using Machine Learning

Maheshwar¹,

Veenadhari²,

Almelu³

2021

IJOSCIENCE

View full text Add to dashboard Cite

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Node Layout Optimization Strategy Based on Aquaculture Water Quality Monitoring System

Jiang,

Sha,

Lin

et al. 2023

Wireless Pers Commun

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Optimized Multi-objective Clustering using Fuzzy Based Genetic Algorithm for Lifetime Maximization of WSN

Pandey,

Singh

2024

RACSC

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Background: Wireless Sensor Networks (WSNs) have gained significant attention due to their diverse applications, including border area security, earthquake detection, and fire detection. WSNs utilize compact sensors to detect environmental events and transmit data to a Base Station (BS) for analysis. Energy consumption during data transmission is a critical issue, which has led to the exploration of additional energy-saving techniques, such as clustering. Objective: The primary objective is to propose an algorithm that selects optimal Cluster Heads (CHs) through a fuzzy-based genetic approach. This algorithm aims to address energy consumption concerns, enhance load balancing, and improve routing efficiency within WSNs. Methods: The proposed algorithm employs a fuzzy-based genetic approach to optimize the selection of CHs for data transmission. Four key parameters are considered: the average remaining energy of CHs, the average distance between CHs and the BS, the average distance between member nodes and CHs, and the standard deviation of the distance between member nodes and CHs. Results: The algorithm's effectiveness is demonstrated through simulation results. When compared to popular models like LEACH, MOEES, and FEEC, it demonstrates an 8-20% improvement in the lifetime of WSNs. The proposed approach achieves enhanced efficiency, lifetime extension, and improved performance in CH selection, load balancing, and routing. Conclusion: In conclusion, this study introduces a novel algorithm that utilizes fuzzy-based genetic techniques to optimize CH selection in WSNs. By considering four key parameters and addressing energy consumption challenges, the proposed algorithm offers significant improvements in efficiency, lifespan, and overall network performance, as validated through simulation results.

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Multi-Objective Heterogeneous Clustering Approach for Efficient-Energy Optimization in WSN

Cited by 3 publications

References 17 publications

Energy Efficient Heterogeneous WNS Clustering Using Machine Learning

Energy Efficient Heterogeneous WNS Clustering Using Machine Learning

Node Layout Optimization Strategy Based on Aquaculture Water Quality Monitoring System

Optimized Multi-objective Clustering using Fuzzy Based Genetic Algorithm for Lifetime Maximization of WSN

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