IoT based Water Quality Monitoring System and Evaluation

Hamid, Shabinar Abdul; Rahim, None Ahmad Mustaqim; Fadhlullah, Solahuddin Yusuf; Abdullah, Syahrul Afzal Che; Muhammad, Zuraida; Leh, Nor Adni Mat

doi:10.1109/iccsce50387.2020.9204931

Cited by 25 publications

(4 citation statements)

References 6 publications

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“…Hamid et al [ 57 ] proposed controlling water quality in real-time to develop an affordable system. Conductivity, pH, turbidity, and the temperature of various chemical and physical water properties are measured via several sensors integrated into the system.…”

Section: Existing Methodsmentioning

confidence: 99%

State of the Art Techniques for Water Quality Monitoring Systems for Fish Ponds Using IoT and Underwater Sensors: A Review

Manoj

Kumar

Arif

et al. 2022

Sensors

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One of the major issues facing the world is the resource of safe water, which is decreasing rapidly due to climatic changes, contamination, and pollution. The most affected living beings are underwater life forms as they eventually take these toxins in and are thus prone to death, making continuously checking water quality a quintessential task. But traditional systems for checking water quality are energy-consuming, involving the initial collection of water samples from different locations and then testing them in the lab. One emerging technology, the Internet of Things (IoT), shows great promise related to this field. This paper presents a detailed review of various water quality monitoring systems (WQSN), using IoT, that have been proposed by various researchers for the past decade (2011–2020). In this instance, new calculations are made for potential clients to analyze the concerned area of research. This review acknowledges key accomplishments concerning quality measures and success indicators regarding qualitative and quantitative measurement. This study also explores the key points and reasons behind lessons learned and proposes a roadmap for impending findings.

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Section: Existing Methodsmentioning

confidence: 99%

State of the Art Techniques for Water Quality Monitoring Systems for Fish Ponds Using IoT and Underwater Sensors: A Review

Manoj

Kumar

Arif

et al. 2022

Sensors

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“…The eyes and skin of pool users may react severely to a high pH level in the water [10,11]. Since all procedures must be carried out manually, the method used to measure the pH level of water at Seberang Jaya Public Swimming Pool is ineffective for maintaining water quality.…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Automatic Water Quality Monitoring System with Optimized Neural Network

2024

KSII TIIS

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One of the biggest dangers in the globe is water contamination. Water is a necessity for human survival. In most cities, the digging of borewells is restricted. In some cities, the borewell is allowed for only drinking water. Hence, the scarcity of drinking water is a vital issue for industries and villas. Most of the water sources in and around the cities are also polluted, and it will cause significant health issues. Real-time quality observation is necessary to guarantee a secure supply of drinking water. We offer a model of a low-cost system of monitoring realtime water quality using IoT to address this issue. The potential for supporting the real world has expanded with the introduction of IoT and other sensors. Multiple sensors make up the suggested system, which is utilized to identify the physical and chemical features of the water. Various sensors can measure the parameters such as temperature, pH, and turbidity. The core controller can process the values measured by sensors. An Arduino model is implemented in the core controller. The sensor data is forwarded to the cloud database using a WI-FI setup. The observed data will be transferred and stored in a cloud-based database for further processing. It wasn't easy to analyze the water quality every time. Hence, an Optimized Neural Network-based automation system identifies water quality from remote locations. The performance of the feed-forward neural network classifier is further enhanced with a hybrid GA-PSO algorithm. The optimized neural network outperforms water quality prediction applications and yields 91% accuracy. The accuracy of the developed model is increased by 20% because of optimizing network parameters compared to the traditional feed-forward neural network. Significant improvement in precision and recall is also evidenced in the proposed work.

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“…The study proposes an IoT-based water quality measurement system, which uses sensors for parameters such as pH, conductivity, salt, and LDR, allowing real-time monitoring and analysis of water quality, with data transmitted through ZigBee and GSM modules, allowing immediate alerts and cloud data storage. S.A. Hamid et al [3] conducted a study on an IoT-based water quality monitoring system and its evaluation. They found that The SWQMS can monitor the water quality automatically and the pH value is stable regardless of time, pool size, or their combination, but the water temperature varies with time.…”

Section: Introductionmentioning

confidence: 99%

IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater

Nishan,

Akter,

Sony

et al. 2024

Preprint

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Industrialization and urbanization both lead to pollution of the environment, particularly of the water. Industrialization and urbanization-related pollution have detrimental consequences both for the environment and for life on Earth. Food poisoning, diarrhea, short-term gastrointestinal problems, respiratory illnesses, skin conditions, and other major health problems can all result from this contaminated water. Most of the waste generated from garment factories is dumped into the closest rivers or canals in a developing nation such as Bangladesh, where the ready-made garment sector is one of the key contributors to the overall gross domestic product (GDP). The quality of water in these bodies has deteriorated to the point that it is no longer suitable for supporting life, making it one of the biggest hazards to both the environment and public health. Furthermore, water pollution in Bangladesh causes a daily decline in the number of fish in Bangladesh's rivers and canals. We must therefore keep an eye on the water quality and identify the causes of pollution if we want to conserve fish, other aquatic life, and the ecosystem. To reduce water pollution, real-time monitoring of water quality is essential. Most methods to reduce water pollution are primarily biological and laboratory-based and require a significant amount of time and resources. To solve this issue, we developed a real-time, three-level water quality monitoring system that is based on the Internet of Things (IoT) and integrated with mobile applications. The system suggested in this study collects data from three layers of wastewater and measures some of the most significant indicators of water, including temperature, turbidity, total dissolved solids (TDS) and hydrogen potential (pH). The outcomes of the proposed system will significantly improve the health living organisms on Earth by reusing wastewater and preserving the environment.

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IoT based Water Quality Monitoring System and Evaluation

Cited by 25 publications

References 6 publications

State of the Art Techniques for Water Quality Monitoring Systems for Fish Ponds Using IoT and Underwater Sensors: A Review

State of the Art Techniques for Water Quality Monitoring Systems for Fish Ponds Using IoT and Underwater Sensors: A Review

IoT-Based Automatic Water Quality Monitoring System with Optimized Neural Network

IoT-Based Three-level Water Quality Assessment System for Industrial Wastewater

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