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Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks Cover

Deterministic Centroid Localization for Improving Energy Efficiency in Wireless Sensor Networks

Open Access
|Apr 2022

Abstract

Wireless sensor networks are an enthralling field of study with numerous applications. A Wireless Sensor Network (WSN) is used to monitor real-time scenarios such as weather, temperature, humidity, and military surveillance. A WSN is composed of several sensor nodes that are responsible for sensing, aggregating, and transmitting data in the system, in which it has been deployed. These sensors are powered by small batteries because they are small. Managing power consumption and extending network life is a common challenge in WSNs. Data transmission is a critical process in a WSN that consumes the majority of the network’s resources. Since the cluster heads in the network are in charge of data transmission, they require more energy. We need to know where these CHs are deployed in order to calculate how much energy they use. The deployment of a WSN can be either static or random. Although most researchers focus on random deployment, this paper applies the proposed Deterministic Centroid algorithm for static deployment. Based on the coverage of the deployment area, this algorithm places the sensors in a predetermined location. The simulation results show how this algorithm generates balanced clusters, improves coverage, and saves energy.

DOI: https://doi.org/10.2478/cait-2022-0002 | Journal eISSN: 1314-4081 | Journal ISSN: 1311-9702
Language: English
Page range: 24 - 39
Submitted on: Jun 30, 2021
Accepted on: Dec 10, 2021
Published on: Apr 10, 2022
Published by: Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2022 Sneha Vijayan, Nagarajan Munusamy, published by Bulgarian Academy of Sciences, Institute of Information and Communication Technologies
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.