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Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification

Author(s): Octarina Nur Samijayani , Tyan Permana Saputra , Hamzah Firdaus , Anwar Mujadin
Author(s) information:
Electrical Engineering Department, Universitas Al Azhar Indonesia, Indonesia, Jakarta. 12110

Corresponding author

Water is essential for human being, also for animals and plants. In Indonesia, there are a lot of residential living in the riverbank which have poor water conditions. People frequenty use water from the river for daily activities. To determine the quality of water, samples are usually taken and tested in the laboratory. This method is less efficient in time and also cost. In order to determine and monitor the quality of water, this paper discuss the Wireless Sensor Network (WSN) to monitor the quality of water from a distance with the self powered sensor node. One of the issue in developing the WSN is the energy. Since this is implemented in outdoor, therefore it is possible to use solar panel to produce the energy. In this study three indicators; pH, TDS, and turbidity; were used to determine water quality based on the Indonesian Minister of Health Regulation. The results examine the WSN performance, and also the analysys of the solar energy supply for each sensor node. The WSN successfully works in detect and clasify tha water quality category and display it in the monitoring center or user. The sensors are calibrated and works with tolerable error of sensor reading of 5,1%. The WSN node is embedded with solar panel to supply the energy for node component. Therefore it able to extend the lifetime of the networks devices with renewable energy to implement the Green WSN.

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About this article

SUBMITTED: 07 April 2023
ACCEPTED: 03 May 2023
PUBLISHED: 9 May 2023
SUBMITTED to ACCEPTED: 26 days
DOI: https://doi.org/10.53623/gisa.v3i1.244

Cite this article
Samijayani, O. N., Saputra, T. P., Firdaus, H. ., & Mujadin, A. . (2023). Solar Powered Wireless Sensor Network for Water Quality Monitoring and Classification. Green Intelligent Systems and Applications, 3(1), 14–21. https://doi.org/10.53623/gisa.v3i1.244
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