IoT and Wireless Sensor Networking-based Effluent Treatment Plant Monitoring System
Open accessAbstract
Contaminated water became a major issue for our country over the last few decades.One of the main reasons behind this scenario is urbanization and industrialization.Every industry should have an Effluent Treatment Plant (ETP) for treating industrial wastewater and safe disposal to the environment.We implement a system that monitors whether an industry uses ETP or not.To monitor ETP, we need to monitor the untreated wastewater quality.The traditional way offers us a method that is time-consuming and inefficient.To solve this problem, we adopt a model based on Wireless Sensor Networking (WSN), which allows us to keep track of the water quality parameters in real-time.This paper proposes a water quality monitoring system that uses WSN and Internet of Things (IoT) based devices to monitor different parameters of water: temperature by a temperature sensor, turbidity by a turbidity sensor, and pH by a pH sensor.Moreover, the microcontroller of Arduino Uno R3 collects the parameter values from these sensors and transmits the values to the IoT based cloud server using the GSM module.The GSM module is also used to alert the supervisors by sending SMS in case of an emergency.Integrating modules such as sensors, Arduino Uno R3, GSM module, enhances the purpose of the desired system.Finally, we calculate the Water Quality Index (WQI) for the pH and turbidity data to report the water quality status.Also, we compare the WQI status with our cloud status, and it shows excellent performance.
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FL offers security features which create an excellent alternative for designing smart and secured IoT devices, especially in light of increasingly severe data privacy protection regulations such as the GDPR.
“FL offers security features which creates an excellent alternative for designing smart and secured IoT devices, especially in light of increasingly severe data privacy protection regulations such as the General Data Protection Regulation (GDPR) [95, 96].”
From Federated learning-based AI approaches in smart healthcare: Concepts, taxonomies, challenges and open issues (2023) · cites this paper as [96]
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Jamrah, A. I., Al-Zghoul, T. M., Aness, W. T., Al‐Qodah, Z., Al-Jameel, G. Q., & Khan, B. (2026) · Water · doi:10.3390/w18202519
- Authors
- Khan, M. S. I., Rahman, A., Islam, S., Nasir, M. K., Band, S. S., & Mosavi, A.
- Affiliations
- University of Dhaka (BD)
- Mawlana Bhashani Science and Technology University (BD)
- Obuda University (HU)
- Corresponding author
- Mosavi, Amir
- Venue
- Acta Polytechnica Hungarica
- Publisher
- Obuda University
- Published
- 2021
- Volume
- 18
- Issue
- 10
- Pages
- 205-224
- ISSN
- 1785-8860 (ISSN-L)
- 2064-2687
- OpenAlex
- W3212948819
- Cited by
- 46
- References
- 45
- Citations by year
- 2026: 3 · 2025: 5 · 2024: 12 · 2023: 14 · 2022: 12
- FWCI
- 2.82
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Acta Polytechnica Hungarica (publishedVersion)
- Topics
- Physical Sciences › Environmental Science › Water Science and Technology › Water Quality Monitoring Technologies
- Physical Sciences › Environmental Science › Waste Management and Disposal › Water Quality Monitoring and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Arduino and IoT Applications
- Keywords
- industrial wastewater, water quality monitoring, wireless sensor networks, Internet of Things, water quality index, turbidity, pH, water temperature, GSM SMS alert, Arduino Uno, cloud-based monitoring, real-time monitoring
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