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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Mechatronics, Electrical Power, and Vehicular Technology TELKOMNIKA (Telecommunication Computing Electronics and Control) Teknika CommIT (Communication & Information Technology) Journal of ICT Research and Applications ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika EMITTER International Journal of Engineering Technology JOIV : International Journal on Informatics Visualization Jurnal Sains Dan Teknologi (SAINTEKBU) INOVTEK POLBENG Jurnal Elektronika dan Telekomunikasi SENTIA 2015 Budimas : Jurnal Pengabdian Masyarakat International Conference on Industrial Revolution for Polytechnic Education Jurnal Nasional Teknik Elektro dan Teknologi Informasi Publikasi Hasil Pengabdian Kepada Masyarakat. Journal Of Artificial Intelligence And Software Engineering Jurnal Pengabdian Nasional (JPN) Indonesia
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Journal : CommIT (Communication

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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
CommIT (Communication and Information Technology) Journal Vol 12, No 2 (2018): CommIT Vol. 12 No. 2 Tahun 2018
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v12i2.4714

Abstract

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%.
An Implementation of Grouping Nodes in Wireless Sensor Network Based on Distance by Using k-Means Clustering Rizqi Fauzil Azhar; Ahmad Zainudin; Prima Kristalina; Bagas Mardiasyah Prakoso; Niam Tamami
CommIT (Communication and Information Technology) Journal Vol. 12 No. 2 (2018): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v12i2.4714

Abstract

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%.
Co-Authors Abd. Rasyid Syamsuri Adam Surya Putra Adam Surya Putra Afifah Dwi Ramadhani Ahmad Zainudin Akuwan Saleh Amang Sudarsono, Amang Anang Budikarso Anang Siswanto, Anang Anisah, Ida Aries Pratiarso Aries Pratiarso, Aries Arifin Arifin Arifin Arifin Azhar, Rizqi Fauzil Bagas Mardiasyah Prakoso Bambang Sumantri Bima Sena Bayu Dewantara Dadio Nugroho Dewi, Florista Dian Neipa Purnamasari Florista Dewi Gamantyo Hendrantoro Hadi, Mochammad Zen Samsono Haryadi Amran I Gede Puja Astawa I Gede Puja Astawa Ida Anisah IMANUDDIN, ACHMAD ILHAM Inka Trisna Dewi Larasati, Vina Lazuardi, Mochamad Riswandha Mahendra, Trisna Agung Mahmudah, Hani'ah Mauridhi Hery P Mauridhi Hery Purnomo Mike Yuliana Mochammad Zen Samsono Hadi Mohamad Ridwan Niam Tamami Niam Tamami Ningsih, Norma Prakoso, Bagas Mardiasyah Putra, Adam Surya Rafina Destiarti Ainul Rahardita Widyatra Ramadhani, Afifah Dwi Reni Soelistijorini Rini Satiti Risa Ekowati Rizqi Fauzil Azhar Sa’adah, Nihayatus Saleh, Akuwan Samsono Hadi, Moch. Zen Satiti, Rini Sa’adah, Nihayatus Soelistijorini, Reni Titon Dutono Tri Budi Santoso Trisna Agung Mahendra Uehara, Hideyuki Umi Fitria P Wiratmoko Yuwono Wirawan Wirawan Yodik Iwan Herlambang