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Energy-Efficient Cluster in Wireless Sensor Network Using Life Time Delay Clustering Algorithms

V. Kumar1,*, N. Jayapandian2, P. Balasubramanie3

1 Department of Computer Science and Engineering, Knowledge Institute of Technology, Salem, India
2 Department of Computer Science and Engineering, Christ University, Bangalore, India
3 Department of Computer Science and Engineering, Kongu Engineering College, Perundurai, India

* Corresponding Author: V. Kumar. Email:

Computer Systems Science and Engineering 2022, 43(1), 77-86.


Through Wireless Sensor Networks (WSN) formation, industrial and academic communities have seen remarkable development in recent decades. One of the most common techniques to derive the best out of wireless sensor networks is to upgrade the operating group. The most important problem is the arrangement of optimal number of sensor nodes as clusters to discuss clustering method. In this method, new client nodes and dynamic methods are used to determine the optimal number of clusters and cluster heads which are to be better organized and proposed to classify each round. Parameters of effective energy use and the ability to decide the best method of attachments are included. The Problem coverage find change ability network route due to which traffic and delays keep the performance to be very high. A newer version of Gravity Analysis Algorithm (GAA) is used to solve this problem. This proposed new approach GAA is introduced to improve network lifetime, increase system energy efficiency and end delay performance. Simulation results show that modified GAA performance is better than other networks and it has more advanced Life Time Delay Clustering Algorithms-LTDCA protocols. The proposed method provides a set of data collection and increased throughput in wireless sensor networks.


Cite This Article

V. Kumar, N. Jayapandian and P. Balasubramanie, "Energy-efficient cluster in wireless sensor network using life time delay clustering algorithms," Computer Systems Science and Engineering, vol. 43, no.1, pp. 77–86, 2022.

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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