A low cost system for real time water quality monitoring and controlling using IoT

2017 ◽  
Author(s):  
K. Gopavanitha ◽  
S. Nagaraju
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Cost System

Smart and Low Cost Real Time Water Quality Monitoring System Using IoT

2019 ◽  
Author(s):  
Jeba Anandh S ◽  
Anandharaj M ◽  
Aswinrajan J ◽  
Karankumar G ◽  
Karthik P
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Monitoring System ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring

scholarly journals Design of smart sensors for real time drinking water quality monitoring and contamination detection in water distributed mains

2017 ◽  
Vol 7 (1.1) ◽  
pp. 47 ◽  
Author(s):  
S. Kavi Priya ◽  
G. Shenbagalakshmi ◽  
T. Revathi
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Water Contamination ◽  
Distribution Systems ◽  
Water Distribution ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Drinking Water Quality ◽  
Quality Monitoring

Drinking Water Distribution Systems facilitate to carry portable water from water resources such as reservoirs, river, and water tanks to industrial, commercial and residential consumers through complex buried pipe networks. Determining the consequences of a water contamination event is an important concern in the field of water systems security and in drinking water distribution systems. The proposed work is based on the development of low cost fuzzy based water quality monitoring system using wireless sensor networks which is capable of measuring physiochemical parameters of water quality such as pH, temperature, conductivity, oxidation reduction potential and turbidity. Based on selected parameters a sensing unit is developed along with several microsystems for analog signal conditioning, data aggregation, sensor data analysis and logging, and remote representation of data to the consumers. Finally, algorithms for fusing the real time data and decision making using fuzzy logic at local level are developed to assess the water contamination risk. Based on the water contamination level in the distribution pipeline the drinking water quality is classified as acceptable/reject/desirable. When the contamination is detected, the sensing unit with ZigBee sends signals to close the solenoid valve inside the pipeline to prevent the flow of contaminated water supply and it intimates the consumers about drinking water quality through mobile app. Experimental results indicate that this low cost real time water quality monitoring system acts as an ideal early warning system with best detection accuracy. The derived solution can also be applied to different IoT (Internet of Things) scenario such as smart cities, the city transport system etc.

Implementation of an innovative sensor technology for effective online water quality monitoring in the distribution network

2012 ◽  
Vol 7 (4) ◽  
Author(s):  
B. R. de Graaf ◽  
F. Williamson ◽  
Marcel Klein Koerkamp ◽  
J. W. Verhoef ◽  
R. Wuestman ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Real Time ◽  
Low Cost ◽  
Distribution Network ◽  
Warning System ◽  
High Sensitivity ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Sensor Technology

For safe supply of drinking water, water quality needs to be monitored online in real time. The consequence of inadequate monitoring can result in substantial health risks, and economic and reputational damages. Therefore, Vitens N.V., the largest drinking water company of the Netherlands, set a goal to explore and invest in the development of intelligent water supply. In order to do this Vitens N.V. has set up a demonstration network for online water quality monitoring, the Vitens Innovation Playground (VIP). With the recent innovative developments in the field of online sensoring Vitens kicked off, in 2011, its first major online sensoring program by implementing a sensor grid based on EventLab systems from Optiqua Technologies Pte Ltd in the distribution network. EventLab utilizes bulk refractive index as a generic parameter for continuous real time monitoring of changes in water quality. Key characteristics of this innovative optical sensor technology, high sensitivity generic sensors at low cost, make it ideal for deployment as an early warning system. This paper describes different components of the system, the technological challenges that were overcome, and presents performance data and conclusions from deployment of Optiqua's EventLab systems in the VIP.

scholarly journals Real-time water quality monitoring of an artificial lake using a portable, affordable, simple, Arduino-based open source sensor

2019 ◽  
Vol 6 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Murat Gökhan Eskin ◽  
Milad Torabfam ◽  
Meral Yüce ◽  
Hasan Kurt ◽  
Alessandra Cincinelli ◽  
...  
Keyword(s):  
Water Quality ◽  
Open Source ◽  
Real Time ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Artificial Lake ◽  
Testing Platform ◽  
Water Testing ◽  
Untreated Wastewater

Water quality assessment is vital to identify existing problems and any changes that emerge in water sources over a period of time. Conventional water quality monitoring systems remain to be limited to on-site sample collection and further analysis in environmental laboratories. The progress in Arduino-based low-cost and open-source hardware has paved the way for the development of low-cost, portable, and on-site measuring platforms. In this work, we have assembled an Arduino-based open-source water testing platform out of commercially available sensors and controllers. The water testing system was powered by a 9 V battery and had the capability of measuring water turbidity, acidity, and temperature on-site in real-time. The calibration and validation studies were carried out to assess the measurement capabilities of turbidity and pH sensors in the lab using calibration samples and UV-Vis-NIR absorption spectroscopy. The water quality platform was tested in an artificial lake that is located at Sabanci University Campus (Istanbul, Turkey), which serves as a reservoir for treated wastewaters and rainwater. Untreated wastewater samples were collected from the wastewater treatment station of the university for comparison. The measurements performed on several locations along the coast of the artificial lake were also validated in the laboratory. The water testing platform showed significant potential for miniaturization and portability of such analytical platforms for on-site environmental monitoring.

A low-cost wireless sensor network for citizen science water quality monitoring

2021 ◽  
Author(s):  
Elena von Benzon ◽  
Elizabeth Bagshaw ◽  
Michael Prior-Jones ◽  
Isaac Sobey ◽  
Rupert Perkins ◽  
...  
Keyword(s):  
Water Quality ◽  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Base Station ◽  
Quality Monitoring ◽  
Wireless Sensor ◽  
Monitoring Equipment

<p>We present the first trial of an accurate, low-cost wireless sensor, the ‘Hydrobean’, and base station designed for use by citizen scientists in catchment water quality monitoring. This novel wireless sensor network addresses key concerns identified with current volunteer monitoring programmes, including temporal discontinuity and insufficient data quality. Hydrobean continuously measures electrical conductivity, temperature and pressure and wirelessly transmits these data to an online portal for observation and download by users. These parameters can be used to assess catchment water quality status, with excursions from baseline conditions detected in real time at high temporal resolution. Citizen scientists have an increasingly important role to play in enhancing our scientific understanding of catchment water quality, but their contribution has so far been limited by barriers to access suitable monitoring equipment. Traditional grab sampling techniques result in key contamination incidents being missed and trend analysis limited as samples are analysed discretely, typically on a weekly or monthly basis. Additionally, the quality of data obtained from basic chemical test kits commonly used by citizen scientists does not meet the requirements of many data users. This research explores the role of low-cost wireless sensor networks in advancing the potential of citizen scientists in monitoring catchment water quality. Monitoring equipment available to citizen scientists generally needs to be low cost, so is unlikely to rival professional standard monitoring techniques in the foreseeable future. However, reliable, low-cost sensors which enable continuous, real-time monitoring do now exist for a limited range of water quality parameters and have been used in the development of the wireless sensor network presented here. Critically, Hydrobean and its base station are low cost, low maintenance, portable and robust in order to meet the requirements of community monitoring programmes. Ultimately, a model will be integrated into the real-time analysis of data collected by the wireless sensor network to predict when and where contamination incidents are expected to be affecting catchment water quality. We report initial field results of the Hydrobean wireless sensor network and will discuss ways in which the basic design can be improved in future versions. </p>

scholarly journals Water Quality Monitoring and Prediction of Water Quality at College Premises using Internet of Things

2019 ◽  
Vol 8 (5S3) ◽  
pp. 53-57
Keyword(s):  
Machine Learning ◽  
Water Quality ◽  
Random Forest ◽  
Real Time ◽  
Multilayer Perceptron ◽  
Low Cost ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Machine Learning Techniques ◽  
Learning Techniques

IoT is becoming more popular and effective tool for any real time application. It has been involved for various water quality monitoring system to maintain the water hygiene level. The main objective is to build a system that regularly monitors the water quality and manages the sustainability. This system deals with specific standards like low cost background and system efficiency when compared to other studies. In this paper, IoT based real time monitoring of water quality system is implemented along with Machine learning techniques such as J48, Multilayer Perceptron (MLP), and Random Forest. These machine learning techniques are compared based on the hyper-parameters and the results were obtained. The attributes such as pH, Dissolved Oxygen (DO), turbidity, conductivity obtained from the corresponding sensors are used to create a prediction model which classifies the quality of water. Measurement of water quality and reporting system is implemented by using Arduino controller, GSM/GPRS module for gathering data in real time. The collected data are then analyzed using WEKA interface which is a visualization tool used for the analysis of data and prediction modeling.The Random forest technique outperforms J48 and Multilayer perceptron by giving 98.89% of correctly classified instances.

Flat Flexible Thin Milli-electrode Array for Real-time in situ Water Quality Monitoring in Distribution Systems

2017 ◽  
Vol 2017 (4) ◽  
pp. 5598-5617
Author(s):  
Zhiheng Xu ◽  
Wangchi Zhou ◽  
Qiuchen Dong ◽  
Yan Li ◽  
Dingyi Cai ◽  
...  
Keyword(s):  
Water Quality ◽  
Real Time ◽  
Distribution Systems ◽  
Electrode Array ◽  
Water Quality Monitoring ◽  
Quality Monitoring
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