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.
Boyd, C.E. (2015) Water Quality an Introduction. Springer: London, UK.
World Health Organization. (2022). Guidelines for drinking-water quality. Geneva: WHO.
Indonesia Ministry of Health. (1990). Regulation of Minister of Health of Indonesia No: 416/MENKES/PER/IX/1990. Republic of Indonesia.
Indonesia Ministry of Health. (2010). Regulation of Minister of Health No.492/MENKES/PER/IV/2010. Republic of Indonesia.
Hoang T. Tran, Nguyen, M.T.; Nguyen, C.V.; Ala, G.; Viola, F.; Colak, I. (2022). Hybrid Solar-RF Energy Harvesting Mechanisms for Remote Sensing Devices. International Journal of Renewable Energy Research, 12, 294304. https://doi.org/10.20508/ijrer.v12i1.12807.g8403.
Mohsen, S. (2022). A Solar Energy Harvester for a Wireless Sensor System toward Environmental Monitoring. Proceedings of Engineering and Technology Innovation, 21, 10. https://doi.org/10.3390/app1203113019.
Tsiropoulos, Z.; Ioannidis, I.G. (2022). A Comparative Analysis between Battery- and Solar-Powered Wireless Sensors for Soil Water Monitoring. Applied Science, 12, 1330. https://doi.org/10.3390/app12031130.
PH meter SKU SEN0161. (accessed on 1 January 2023) Available online: https://wiki.dfrobot.com/PH_meter_SKU__SEN0161_?gclid=EAIaIQobChMIzYakoa7S4QIVxg0rCh2_xQvCEAAYAiAAEgK4e_D_BwE.
Do, H.T.; Lo, S.-L.; Thi, L.A.P. (2013). Calculating of river water quality sampling frequency by the analytic hierarchy process (AHP). Environmental Monitoring and Assessment, 185, 909. https://doi.org/10.1007/s10661-012-2600-6.
Ahmeda, A.N. (2019). Machine learning methods for better water quality prediction. Journal of Hydrology, 578, 124084. https://doi.org/10.1016/j.jhydrol.2019.124084.
Chowdurya, M.S.U.; Emranb, T.B.; Pathak, S.G.A.; Alam, M.M. (2019). IoT Based Real-time River Water Quality Monitoring System. Procedia Computer Science, 155, 161168. https://doi.org/10.1016/j.procs.2019.08.025.
Chen, Y.; Song, L.; Liu, Y.; Yang, L.; Li, D. (2020). A Review of the Artificial Neural Network Models for Water Quality Prediction. Applied Sciences, 10, 5776. https://doi.org/10.3390/app10175776.
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