Smart Water Quality Monitoring Research Articles

FPGA Implementation of Smart Water Quality Monitoring System

Water quality is important to our health and well- being .Water quality monitoring plays a vital role in keeping the planet healthy and sustainable. Due to increasing industrialization and environmental imbalance, water quality and fresh water quantity are decreasing day by day. In this paper, a reconfigurable hardware system is developed to monitor the water quality and physical parameters are monitored using temperature sensor and turbidity sensor; chemical parameters are monitored using pH sensor; in addition CO2 and water level sensors are used. These sensors are interfaced with the reconfigurable hardware i.e., FPGA. This FPGA is used to monitor the water related parameters such as water quality, waste water parameters and also used in agricultural fields. The hardware is implemented using Verilog HDL. Wireless Sensor Network enables interface between human and computer by means of wireless link. The output is displayed in the personal computer.

IoT Based Smart Water Quality Monitoring and Prediction System

As indicated by Human Rights Watch, twenty million individuals in our nation are as yet drinking water defiled with arsenic. The World wellbeing Organization (WHO) has likewise expressed this emergency as "the biggest mass harming of a populace ever". To diminish the water related ailments and avoid water populace, we need to quantify water parameters, for example, ph, turbidity, conductivity, temperature and so on. Conventional approach of water observing requires gathering information from different sources physically. A while later examples will send lab for testing and breaking down. So as to spare time utilization and diminishing manual exertion my testing supplies will be put in any water source. Thus, this model can distinguish contamination remotely and take essential activities. The primary objective of this paper to assemble a Sensor-based Water Quality Monitoring System. Arduino Mega 2560 go about as a base station and information from sensor hubs will be send to it. For the scholastic reason, this paper exhibits a little model of sensor systems comprising of temperature, water level, stream and ph. At that point ph and temperature sensor esteems were sent cloud stage (ARTIK cloud) and showed as a graphical portrayal on a neighborhood PC. In addition, GSM shield (SIM808) is associated with Arduino Mega which thinks about sensor esteems to edge esteems and sends a text-based notification to the operator if the got esteem is above or underneath the edge esteem. The aftereffects of this undertaking are talked about in the outcome area of the paper. We tried three water tests from three diverse water sources, (for example, modern water, faucet water and pool water). Three water tests gathered from three distinctive swimming pools.(Except one example) Ph esteem found in rest of the examples were in typical range (temperature esteem between 26-27'C). Result segment (in page 20) clarifies our venture discoveries in subtleties.

Risk Assessment of Water Accidental Contamination Using Smart Water Quality Monitoring

Accidental contamination and malicious attacks can degrade the water quality in water distribution networks and threat the human health. Therefore, a rapid detection of water contamination is required to prevent waterborne diseases. The use of water quality sensors allows a real-time monitoring of several physical and chemical parameters. The aim of this paper is to combine the smart monitoring with the risk assessment approach to ensure early detection of water contamination. Within the European Project “SmartWater4Europe,” S::CAN sensor was implemented, on the water supply system of the Campus of Lille University, France, since 2016. The campus is a representative field study with 15 km of water distribution network. In this paper, Turbidity and Chlorine, recorded online by S::CAN, were analyzed continuously to define the risk assessment parameters (the severity of consequences and the likelihood of an event). The application of the proposed approach indicates that the magnitude of deviation from thresholds limits and the duration of events are the two essential parameters to be considered in risk assessment approach. The paper shows that this new approach provides promising perspective for the early detection of the water contamination. It allows to identify the risk level and the priority required in real time, without resorting laboratory analyses.

Analysis and Prediction of Water Quality Using LSTM Deep Neural Networks in IoT Environment

This research paper focuses on a water quality prediction model which requires high-quality data. In the process of construction and operation of smart water quality monitoring systems based on Internet of Things (IoT), more and more big data are produced at a high speed, which has made water quality data complicated. Taking advantage of the good performance of long short-term memory (LSTM) deep neural networks in time-series prediction, a drinking-water quality model was designed and established to predict water quality big data with the help of the advanced deep learning (DL) theory in this paper. The drinking-water quality data measured by the automatic water quality monitoring station of Guazhou Water Source of the Yangtze River in Yangzhou were utilized to analyze the water quality parameters in detail, and the prediction model was trained and tested with monitoring data from January 2016 to June 2018. The results of the study indicate that the predicted values of the model and the actual values were in good agreement and accurately revealed the future developing trend of water quality, showing the feasibility and effectiveness of using LSTM deep neural networks to predict the quality of drinking water.

A Study on IOT for Smart Water Quality Monitoring Using MQTT Algorithm

The decrease in quality of water resources has become a common problem. The standard methods of water quality surveillance include water sample manual collection from various locations. These water samples were tested in laboratory using intelligence capabilities. Such approaches take time and are no longer considered inefficient. The old method of water quality detection was time consuming, less accurate and expensive. By focusing on the above problems, IOT can be used to monitor water quality in real time, a low cost water quality monitoring system. Water quality parameters in the proposed system are measured by various sensors such as pH, temperature and dissolved oxygen to transfer data on a platform via a microcontroller system. Therefore, to meet these needs, you can use other technologies such as MQTT (Message Sorting Delimiter Transform), allowing the Sensor and End device rankings to publish and subscribe. And the number of data simultaneously between sensors and servers with the help of the MQTT algorithm.

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

Now a day’s water pollution is one of the biggest fears for the green globalization. To prevent the water pollution, first we have to estimate the water parameters like pH, turbidity, temperature and TDS as the variations in the values of these parameters point towards the presence of pollutants. In this project we design and develop a low cost system for real time monitoring of the water quality in IoT. Nowadays Internet of Things (IoT) and Remote Sensing (RS) techniques are used in different area of research for monitoring, collecting and analysis data from remote locations. This project proposes a Sensor-Based Water Quality Monitoring System which is used for measuring physical and chemical parameters of the water. The parameters such as Temperature, pH, TDS and water level of the water can be measured and the system provides a visual image of interior part of water container using raspberry pi camera. The measured values from the sensors can be processed by the core controller. The Raspberry Pi model can be used as a core controller. Finally, the sensor data can be viewed on internet using cloud storage (Thingspeak). The system also provides an alert to a remote user, when there is a deviation of water quality parameters from the predefined set of standard values. The uniqueness of our proposed paper is to obtain the water monitoring system with high frequency, high mobility, and low powered.

Smart Water Quality Monitoring System Using Iot Technology

The economical and effective system of water quality observation is the most robust implementation of impure water. Drinking water could be precious for all people as water utilities face more challenges. These challenges arise due to the high population, fewer water resources, etc. So, different methods are used to monitor in the real-time water quality. To make sure that safe distribution of water is done, it must be observed in real time for a new method in the “Internet of Things (IoT)” based water quality has been projected. Real-time water quality observation is examined by data acquisition, method, and transmission with an increase in the wireless device network method in the IoT. Microcontroller and the processed values remotely to the core controller ARM with a WI-FI protocol are used to interface the measured values from the sensors. This projected the water quality observation interface sensors with quality observation with IOT setting. WQM selects parameters of water like temperature, pH level, water level and CO2 by multiple different device nodes. This methodology sends the information to the web server. The data updated at intervals within the server may be retrieved or accessed from anyplace within the world. If the sensors do not work or get into abnormal conditions, then a buzzer will be ON.

Internet of Things (Iot) Based Smart Water Quality Monitoring System

The economical and effective system of water quality observation is the toughest implementation of impure water. Drinking water could be terribly precious for all people as water utilities face more challenges. These challenges arise due to high population, less water resources etc. So, different methods are used to monitor in the real time water quality. To make sure that safe distribution of water is done, it should be monitored in real time for new approach in IOT based water quality has been projected. Real time water quality observation is monitored by data acquisition, method and transmission with increase in the wireless device network technology in internet of things. The measured values from the sensors are interfaced by microcontroller and the processed values remotely to the core controller ARM with a WI-FI protocol. This projected water quality observation interfaces sensors with quality observation with IOT setting. WQM selects parameters of water like temperature, pH level, water level and CO2 by multiple different device nodes. This methodology sends the information to the web server. The data updated at intervals within the server may be retrieved or accessed from anyplace within the world. If the sensors do not work or get into abnormal conditions then a buzzer will be ON.

Internet of things enabled real time water quality monitoring system

AbstractSmart solutions for water quality monitoring are gaining importance with advancement in communication technology. This paper presents a detailed overview of recent works carried out in the field of smart water quality monitoring. Also, a power efficient, simpler solution for in-pipe water quality monitoring based on Internet of Things technology is presented. The model developed is used for testing water samples and the data uploaded over the Internet are analyzed. The system also provides an alert to a remote user, when there is a deviation of water quality parameters from the pre-defined set of standard values.

Design of IPv6 Network enabled Smart Water Flow Meter System for India

Water is one of the major requirements for human survival. During the past decade, water needs have risen exponentially to an unprecedented scale in India. The rapid urban development, increasing human population, climate change and wasteful usage have led to severe water shortages. The solution lies largely in areas of effective mechanisms for conservation, distribution, efficient use and management of this resource. Ensuring access to safe and clean water is another issue that requires attention. In this paper, we present an IPv6 network connected design for real-time water flow metering and quality monitoring system. Our prototype implementation uses the emerging HTTP-compatible CoAP based monitoring and control approach for seamless web integration, thus supporting internet based data collection. The system addresses new challenges in the water sector - ease of billing, fair billing and the need for a study of supply versus consumption of water in order to create awareness to curb water wastage and encourage its conservation. The traditional water metering systems require periodic manual intervention for both metering and maintenance making it inconvenient and often least effective. Shortcomings of the existing models call for a ubiquitous usage of wireless systems for smart water flow metering and quality monitoring. We propose to do this with the aid of CC2538 motes that monitor the water consumption and communicate the data to a gateway wirelessly so that the data can

A survey of smart water quality monitoring system.

The smart water quality monitoring, regarded as the future water quality monitoring technology, catalyzes progress in the capabilities of data collection, communication, data analysis, and early warning. In this article, we survey the literature till 2014 on the enabling technologies for the Smart Water Quality Monitoring System. We explore three major subsystems, namely the data collection subsystem, the data transmission subsystem, and the data management subsystem from the view of data acquiring, data transmission, and data analysis. Specifically, for the data collection subsystem, we explore selection of water quality parameters, existing technology of online water quality monitoring, identification of the locations of sampling stations, and determination of the sampling frequencies. For the data transmission system, we explore data transmission network architecture and data communication management. For the data management subsystem, we explore water quality analysis and prediction, water quality evaluation, and water quality data storage. We also propose possible challenges and future directions for each subsystem.