Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)

Wireless sensor networks WSNs have expanded in popularity in recent years and are now being utilized for many different operational tasks including tracking, monitoring, transportation, military operations, and healthcare. Therefore, it's essential for WSNs to prolong the sensor node's li...

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Main Authors: Dhulfiqar Talib Abbas, Dalal Abdulmohsin Hammood, Seham ahmed hashem, Saidatul Norlyana Azemi
Format: Article
Language:English
Published: middle technical university 2023-06-01
Series:Journal of Techniques
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Online Access:https://journal.mtu.edu.iq/index.php/MTU/article/view/693
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author Dhulfiqar Talib Abbas
Dalal Abdulmohsin Hammood
Seham ahmed hashem
Saidatul Norlyana Azemi
author_facet Dhulfiqar Talib Abbas
Dalal Abdulmohsin Hammood
Seham ahmed hashem
Saidatul Norlyana Azemi
author_sort Dhulfiqar Talib Abbas
collection DOAJ
description Wireless sensor networks WSNs have expanded in popularity in recent years and are now being utilized for many different operational tasks including tracking, monitoring, transportation, military operations, and healthcare. Therefore, it's essential for WSNs to prolong the sensor node's lifespan. The most crucial component in the sensor nodes is the energy from the battery, determining how long the WSN will last. Energy saving is essential since there is a limited battery powering the sensor nodes. Energy is needed at sensor nodes for a variety of operations, including data receipt and transmission, data processing, sensing, and other operations. However, data processing uses substantially less energy than data transmission, which has the highest energy consumption of all of them. As a result, reducing the spacing between the base station (BS) and the sensor node will result in reducing the distance that the data travels on its way to the BS, which will help conserve energy and increase the lifespan of WSNs. In this research, two methods that operate at the sensor node level are proposed: clustering and data aggregation. K-means clustering and Extrema Point (EP) data aggregation. The proposed approaches operate in three steps periodically: data collection, data aggregation, and data transmission. By aggregating duplicated data before transmitting, it to the base station (BS), these methods aim to shorten the distance between sensor nodes and the base station as well as the amount of transmitted data, while maintaining a reasonable level of accuracy for the data received at the BS or Cluster Head (CH). The efficiency of the proposed strategies is evaluated by extensive simulated experiments. The simulation outcomes demonstrate that the proposed methodology outperforms the current strategies and yields energy savings of over 90% when compared to the PIP-DA and ATP strategies.
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spelling doaj-art-6cb220225e18474b80c2331a191661472025-01-19T11:01:54Zengmiddle technical universityJournal of Techniques1818-653X2708-83832023-06-015210.51173/jt.v5i2.693Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)Dhulfiqar Talib Abbas0Dalal Abdulmohsin Hammood1Seham ahmed hashem2Saidatul Norlyana Azemi3Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Electrical Engineering Technical College, Middle Technical University, Baghdad, Iraq.Technical Instructors Training Institute, Middle Technical University, Baghdad, IraqUniversiti Malaysia Perlis, Kampus Pauh Putra, UniMAP, 02600 Arau, Perlis, Malaysia Wireless sensor networks WSNs have expanded in popularity in recent years and are now being utilized for many different operational tasks including tracking, monitoring, transportation, military operations, and healthcare. Therefore, it's essential for WSNs to prolong the sensor node's lifespan. The most crucial component in the sensor nodes is the energy from the battery, determining how long the WSN will last. Energy saving is essential since there is a limited battery powering the sensor nodes. Energy is needed at sensor nodes for a variety of operations, including data receipt and transmission, data processing, sensing, and other operations. However, data processing uses substantially less energy than data transmission, which has the highest energy consumption of all of them. As a result, reducing the spacing between the base station (BS) and the sensor node will result in reducing the distance that the data travels on its way to the BS, which will help conserve energy and increase the lifespan of WSNs. In this research, two methods that operate at the sensor node level are proposed: clustering and data aggregation. K-means clustering and Extrema Point (EP) data aggregation. The proposed approaches operate in three steps periodically: data collection, data aggregation, and data transmission. By aggregating duplicated data before transmitting, it to the base station (BS), these methods aim to shorten the distance between sensor nodes and the base station as well as the amount of transmitted data, while maintaining a reasonable level of accuracy for the data received at the BS or Cluster Head (CH). The efficiency of the proposed strategies is evaluated by extensive simulated experiments. The simulation outcomes demonstrate that the proposed methodology outperforms the current strategies and yields energy savings of over 90% when compared to the PIP-DA and ATP strategies. https://journal.mtu.edu.iq/index.php/MTU/article/view/693Energy SavingK-MeansClusteringElbowWSNData Reduction
spellingShingle Dhulfiqar Talib Abbas
Dalal Abdulmohsin Hammood
Seham ahmed hashem
Saidatul Norlyana Azemi
Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
Journal of Techniques
Energy Saving
K-Means
Clustering
Elbow
WSN
Data Reduction
title Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
title_full Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
title_fullStr Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
title_full_unstemmed Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
title_short Minimizing Energy Consumption Based on Clustering & Data Aggregation Technique in WSN (MECCLADA)
title_sort minimizing energy consumption based on clustering data aggregation technique in wsn mecclada
topic Energy Saving
K-Means
Clustering
Elbow
WSN
Data Reduction
url https://journal.mtu.edu.iq/index.php/MTU/article/view/693
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