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Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN

M. Manikandan1,*, S. Sakthivel2, V. Vivekanandhan1

1 Department of Computer Science and Engineering, Adhiyamaan College of Engineering, Hosur, 635109, Tamilnadu, India
2 Department of Computer Science and Engineering, Sona College of Technology, Salem, 636005, Tamilnadu, India

* Corresponding Author: M. Manikandan. Email: email

Intelligent Automation & Soft Computing 2023, 35(3), 3169-3185. https://doi.org/10.32604/iasc.2023.029232

Abstract

Wireless Sensor Networks are composed of autonomous sensing devices which are interconnected to form a closed network. This closed network is intended to share sensitive location-centric information from a source node to the base station through efficient routing mechanisms. The efficiency of the sensor node is energy bounded, acts as a concentrated area for most researchers to offer a solution for the early draining power of sensors. Network management plays a significant role in wireless sensor networks, which was obsessed with the factors like the reliability of the network, resource management, energy-efficient routing, and scalability of services. The topology of the wireless sensor networks acts driven factor for network efficiency which can be effectively maintained by performing the clustering process effectively. More solutions and clustering algorithms have been offered by various researchers, but the concern of reduced efficiency in the routing process and network management still exists. This research paper offers a hybrid algorithm composed of a memetic algorithm which is an enhanced version of a genetic algorithm integrated with the adaptive hill-climbing algorithm for performing energy-efficient clustering process in the wireless sensor networks. The memetic algorithm employs a local searching methodology to mitigate the premature convergence, while the adaptive hill-climbing algorithm is a local search algorithm that persistently migrates towards the increased elevation to determine the peak of the mountain (i.e.,) best cluster head in the wireless sensor networks. The proposed hybrid algorithm is compared with the state of art clustering algorithm to prove that the proposed algorithm outperforms in terms of a network life-time, energy consumption, throughput, etc.

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APA Style
Manikandan, M., Sakthivel, S., Vivekanandhan, V. (2023). Efficient clustering using memetic adaptive hill climbing algorithm in WSN. Intelligent Automation & Soft Computing, 35(3), 3169-3185. https://doi.org/10.32604/iasc.2023.029232
Vancouver Style
Manikandan M, Sakthivel S, Vivekanandhan V. Efficient clustering using memetic adaptive hill climbing algorithm in WSN. Intell Automat Soft Comput . 2023;35(3):3169-3185 https://doi.org/10.32604/iasc.2023.029232
IEEE Style
M. Manikandan, S. Sakthivel, and V. Vivekanandhan, “Efficient Clustering Using Memetic Adaptive Hill Climbing Algorithm in WSN,” Intell. Automat. Soft Comput. , vol. 35, no. 3, pp. 3169-3185, 2023. https://doi.org/10.32604/iasc.2023.029232



cc Copyright © 2023 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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