Implementation of Water Quality Sensing System using Internet of Things

Arvind, Arnav; Paul, Rajtirtha; Bhulania, Paurush

doi:10.1109/spin48934.2020.9070832

Cited by 7 publications

(3 citation statements)

References 4 publications

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“… Sarnin et al [ 107 ] 2020 GSM Temp and humidity. Arvind et al [ 108 ] 2020 Wi-Fi pH, TDS, TUR and Temp. Bai et al [ 109 ] 2020 Zigbee/GSM pH and Temp Yunfeng and Tianpei [ 110 ] 2019 NB-IoT DO Jo et al [ 55 ] 2019 Bluetooth Temp, TUR and pH Simitha and Subodh Raj [ 72 ] 2019 LoRaWAN pH, TUR, Temp and DO Madeo et al [ 71 ] 2020 LoRaWAN Temp, pH, Salinity, ORP and DO.…”

Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

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Section: Resultsmentioning

confidence: 99%

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Essamlali,

Nhaila,

El Khaili

2024

Heliyon

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“…Real-time water quality monitoring in the IoT environment has been attempted by many researchers. Vijaykumar and Ramya [5] and Arvind et al [6] proposed a water quality monitoring system for real-time assessment based on the IoT platform. Vijaykumar used the "ioBridge" as an IoT platform, whereas Arvind used the "ThingSpeak" IoT platform.…”

Section: Related Workmentioning

confidence: 99%

Assessment of Water Quality Parameters in Real-Time Environment

Khatri

Gupta

2020

SN COMPUT. SCI.

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Assessment of drinking water quality has been an important issue nowadays as the water available is severely polluted and can be the cause of diseases like cholera, diarrhea, dysentery, etc. The traditional methods for water quality monitoring require a high-labor-cost and tine consumption as these methods include a sample collection followed by lab-based chemical testing. In addition, the chemicals used in the testing are toxic and of high-cost. So, there is a need for real-time monitoring and chemical-free testing of water quality parameters. This paper presents a real-time water quality monitoring system based on the Raspberry Pi 3 development board and a Python framework. The water quality parameters utilized for water quality monitoring are temperature, pH, oxidation reduction potential, electrical conductivity, and dissolved oxygen and E. coli. The water quality sensors were interfaced with the designed embedded platform. Finally, the acquired parameters were compared with the benchmark equipment for validation.

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“…The World Health Organization (WHO), the drinking water purity standard is a TDS level below 100 ppm (Raghavan et al, 2017). Good water has a TDS level of less than 300-600 ppm (Part Per Million), which has relatively small mineral components (Arvind et al, 2020). If water has a TDS level of 900-1200 ppm (Parts Per Million) or more, it means that the water contains relatively large minerals and is hazardous to health (Pant et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Effect Mass of Silica Sand on Reducing Fe Concentration in Water Purification Systems

Suparno,

Simamora

2023

jppipa, pendidikan ipa, fisika, biologi, kimia

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This study aims to determine the effect of silica sand mass in the air purification system on the decrease in Fe concentration. The type of research used is experimental research. The water purification media used in the research were silica sand and gravel. The mass of gravel used in the water purification system is constant, namely 500 grams. The mass of silica sand used varies, namely 200 grams, 400 grams, 600 grams, 800 grams and 1000 grams. The research was carried out through three stages, namely the stages of preparation, implementation, and data collection. The effect of the mass of silica sand in the air purification system on decreasing the concentration of Fe showed positive results. The results of research with a fixed mass of gravel and varying masses of silica sand showed a decrease in Fe concentration after water purification with a value that met environmental health quality standards and water health requirements Fe (iron) content below 1 mg/L or ppm so that the water worth using.

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Implementation of Water Quality Sensing System using Internet of Things

Cited by 7 publications

References 4 publications

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Advances in machine learning and IoT for water quality monitoring: A comprehensive review

Assessment of Water Quality Parameters in Real-Time Environment

Effect Mass of Silica Sand on Reducing Fe Concentration in Water Purification Systems

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