Real-Time Pond Water Quality Monitoring Device Design Using Hybrid Wireless Local Area Network And Cellular Technology
Keywords:
Hybrid WLAN-GSM Network, Monitoring, Aquaculture, Pond Water, IOTAbstract
Climate change has increased environmental uncertainty, posing significant challenges to aquaculture sustainability in Indonesia, a coastal country where fishery resources play a vital role in food security and livelihoods. Despite high fish demand, pond productivity continues to decline due to suboptimal water quality management, limited monitoring accessibility, and the high cost of conventional monitoring systems. This study aims to develop a simple and cost-effective real-time pond water quality monitoring system using a hybrid Wireless Local Area Network (WLAN) and Global System for Mobile Communications (GSM) architecture. Water quality data were obtained through continuous measurements of pH, total dissolved solids (TDS), and water temperature using integrated sensors connected to an Arduino Uno R4 WiFi. The collected data were transmitted in real time via a hybrid WLAN–GSM communication system to enable remote and continuous monitoring. Experimental results show that the system achieved accuracy rates of 94.31% for temperature, 94.91% for TDS, and 98.13% for pH measurements. These results indicate that the proposed system performs reliably and can support effective pond management to optimize fish growth and enhance aquaculture productivity.
Author Biographies
Hana A R, Electrical Engineering Department, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia
Hana A. R. is currently pursuing a Bachelor of Engineering (B.Eng.) in Electronics Engineering at Universitas Negeri Surabaya. He is an active member of the IEEE Student Branch at Universitas Negeri Surabaya. His research interests focus on artificial intelligence. He can be contacted via email at [email protected].
Hapsari Peni Agustin Tjahyaningtijas, Electrical Engineering Department, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia
Hapsari Peni Agustin Tjahyaningtijas received the Bachelor of Physics (B.Sc.) in Physics, Master of Engineering (M.Eng.) in Multimedia Telecommunication, and Doctoral degree in Electronics Engineering from Institut Teknologi Sepuluh Nopember (ITS), Surabaya, Indonesia. Her research interests include deep learning, computer vision, and machine vision. She can be contacted at email: [email protected].
Bambang Suprianto , Electrical Engineering Department, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia
Bambang Suprianto He is a lecturer in the Department of Electrical Engineering, Universitas Negeri Surabaya, Indonesia. He completed his Bachelor’s degree in Electronic Engineering Education at Universitas Negeri Surabaya in 1986. He obtained his Master of Engineering degree from Sepuluh Nopember Institute of Technology in 2001 and completed his Doctorate in Electrical Engineering at the same institution in 2012. His research interests include power systems, control systems, and electronics. He can be contacted via email at [email protected].
Lilik Anifah , Electrical Engineering Department, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia
Lilik Anifah was born in Gresik, East Java, Indonesia in 1979. He received the B.S. in Engineering Physics, and M.S. and Doctoral degrees in Electrical Engineering from the Institut Teknologi Sepuluh Nopember in 2007 and 2013. From 2003 to present, she is a lecturer in Electrical Engineering, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia. Her research interests include intelligence pattern recognition, applications of artificial intelligence, image processing, and smart systems.
Muhammad Aamir Nashrullah , Electrical Engineering Department, Faculty of Engineering, Universitas Negeri Surabaya, Indonesia
Muhammad ‘Aamir Nashrullah Received the Bachelor of Engineering (B.Eng.) and currently pursuing a Master of Engineering (M.Eng.) in Electronics Engineering at Universitas Negeri Surabaya. He is an active member of the IEEE Student Branch at Universitas Negeri Surabaya. His research interests focus on artificial intelligence. He can be contacted via email at [email protected].
Andi Kurniawan Nugroho, Electrical Engineering Deparment, Faculty of Engineering, Universitas Semarang, Indonesia
Andi Kurniawan Nugroho is a lecturer in the Department of Electrical Engineering, Faculty of Engineering, Universitas Semarang. He obtained his Bachelor and Master degrees in Electrical Engineering and later earned a Doctoral degree from the Institut Teknologi Sepuluh Nopember. His research interests include medical image and signal analysis, artificial intelligence-based health technology, Internet of Things (IoT), robotics, machine learning, and image processing. He has conducted various studies related to stroke classification using MRI images, deep learning, convolutional neural networks (CNN), and intelligent healthcare systems.
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