An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering

Azhar, Rizqi Fauzil; Zainudin, Ahmad; Kristalina, Prima; Prakoso, Bagas Mardiasyah; Tamami, Niam

doi:10.21512/commit.v12i2.4714

Jurnal SINTA 1

An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering

CommIT (Communication and Information Technology) Journal · Vol. 12 No. 2 (2018) · hlm. 97–104

DOI: 10.21512/commit.v12i2.4714
ISSN: 2460-7010, 1979-2484
Terbit: 2018-10-31
Penerbit: Bina Nusantara University
Bahasa: eng

Abstrak

Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%.

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14 penulis · 31 tautan kepenulisan bersama · 2 lintas organisasi

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Politeknik Elektronika Negeri Surabaya
Institut Teknologi Sepuluh Nopember

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Rekan penulis	Organisasi	Artikel bersama
Wirawan Wirawan	Institut Teknologi Sepuluh Nopember	1
Gamantyo Hendrantoro	Institut Teknologi Sepuluh Nopember	1
Hary Oktavianto	Politeknik Elektronika Negeri Surabaya	1
Haniif Mulya Wicaksana	Politeknik Elektronika Negeri Surabaya	1
Audra Annisa Zhafirah	Politeknik Elektronika Negeri Surabaya	1
Mohammad Syafrudin	Politeknik Elektronika Negeri Surabaya	1
BAMBANG SUMANTRI	Politeknik Elektronika Negeri Surabaya	1
Dafit Ody Endriantono	Politeknik Elektronika Negeri Surabaya	1
Dedid Cahya Happyanto	Politeknik Elektronika Negeri Surabaya	1

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Sumber metadata: OAI-PMH (metadata jurnal) https://journal.binus.ac.id/index.php/commit/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.

Metadata asli (oai_dc)

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	<dc:title xml:lang="en-US">An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering</dc:title>
	<dc:creator>Azhar, Rizqi Fauzil</dc:creator>
	<dc:creator>Zainudin, Ahmad</dc:creator>
	<dc:creator>Kristalina, Prima</dc:creator>
	<dc:creator>Prakoso, Bagas Mardiasyah</dc:creator>
	<dc:creator>Tamami, Niam</dc:creator>
	<dc:subject xml:lang="en-US">Wireless Sensor Network</dc:subject>
	<dc:subject xml:lang="en-US">Cluster- Based</dc:subject>
	<dc:subject xml:lang="en-US">k-Nearest Neighbour</dc:subject>
	<dc:description xml:lang="en-US">Wireless Sensor Network (WSN) is a network consisting of several sensor nodes that communicate with each other and work together to collect data from the surrounding environment. One of the WSN problems is the limited available power. Therefore, nodes on WSN need to communicate by using a cluster-based routing protocol. To solve this, the researchers propose a node grouping based on distance by using k-means clustering with a hardware implementation. Cluster formation and member node selection are performed based on the nearest device of the sensor node to the cluster head. The k-means algorithm utilizes Euclidean distance as the main grouping nodes parameter obtained from the conversion of the Received Signal Strength Indication (RSSI) into the distance estimation between nodes. RSSI as the parameter of nearest neighbor nodes uses lognormal shadowing channel modeling method that can be used to get the path loss exponent in an observation area. The estimated distance in the observation area has 27.9% error. The average time required for grouping is 58.54 s. Meanwhile, the average time used to retrieve coordinate data on each cluster to the database is 45.54 s. In the system, the most time-consuming process is the PAN ID change process with an average time of 14.20 s for each change of PAN ID. The grouping nodes in WSN using k-means clustering algorithm can improve the power efficiency by 6.5%.</dc:description>
	<dc:publisher xml:lang="en-US">Bina Nusantara University</dc:publisher>
	<dc:date>2018-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.binus.ac.id/index.php/commit/article/view/4714</dc:identifier>
	<dc:identifier>10.21512/commit.v12i2.4714</dc:identifier>
	<dc:source xml:lang="en-US">CommIT (Communication and Information Technology) Journal; Vol. 12 No. 2 (2018): CommIT Journal; 97-104</dc:source>
	<dc:source>2460-7010</dc:source>
	<dc:source>1979-2484</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.binus.ac.id/index.php/commit/article/view/4714/3466</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 CommIT (Communication and Information Technology) Journal</dc:rights>
</oai_dc:dc>