scholarly journals Development and Implementation of Water Quality Assessment Monitoring (WQAM) System using the Internet of Things (IoT) in Water Environment

2021 ◽  
Vol 2 (2) ◽  
Author(s):  
Muhammad Farhan Johan ◽  
◽  
Samihah Abdullah ◽  
Nor Shahanim Mohamad Hadis ◽  
Saodah Omar ◽  
...  
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Quality Assessment ◽  
Real Time ◽  
Technology Implementation ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Time Data ◽  
Cloud Server ◽  
Real Time Data

Water quality monitoring (WQM) system is widely being explored as it is needed to prevent the problem of water contamination worldwide. Nowadays, there are various studies on WQM system that are being integrated with Internet of Things (IoT) concept for Wireless Sensor Network (WSN) technology implementation to get real time data measurement. Traditional ways of collecting the data are more time consuming and they lack real time changes in the quality of water. This paper presents the development and implementation of Water Quality Assessment and Monitoring (WQAM) system. The system development used WiFi enabled microcontroller to connect with the IoT environment and store the data in the IoT cloud server. The microcontroller used is Arduino UNO that interacts with three types of sensor probes which are pH, turbidity and temperature probe. All the data measurements is transferred using a WiFi module which is ESP8266. The IoT cloud used to utilize the data frame is ThingSpeak. This system was implemented on Bandar Perda Lake and Derhaka River in Pulau Pinang with two systems implemented at each location. The sensors were placed on the water surface for more accurate measurements. This system continuously measures the readings of pH, turbidity dan temperature on the lake/river for every 1 hour. Twenty readings were taken for every 1 hour within the first 20 minutes with 1 minute interval and the readings were stored in the IoT cloud server. The readings are accessible via ThingSpeak GUI. In conclusion, this system would benefit the authorities to take advantage of using the WQAM system with the aid of the IoT that is less time consuming, less cost and more reliable in real time data reading.

Real time data quality control applied on an IOT sensor water quality network

2020 ◽  
Author(s):  
Matthias Maeyens ◽  
Brianna Pagán ◽  
Piet Seuntjens ◽  
Bino Maiheu ◽  
Nele Desmet ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Control ◽  
Surface Water ◽  
Data Quality ◽  
Real Time ◽  
Water Quality Monitoring ◽  
Data Quality Control ◽  
Time Data ◽  
Data Set ◽  
Real Time Data

<p>In recent years, extend periods of drought have been affecting the water quality and availability in  the Flanders region in Belgium. Especially the coastal region experienced an increased salinization of ground and surface water. The Flemish government therefore decided to invest in a dense IoT water quality monitoring network aiming to deploy 2500 water quality sensors  primarily in surface water but also in ground water and sewers. The goal of this "Internet of Water" project is to establish an operational state of the art monitoring and prediction system in support of future water policy in Flanders. </p><p>Since Flanders is a relatively small region (13,522 km²), placing this many sensors will result in one of the most dense surface water quality sensor networks in the world. Each sensor will continuously measure several indicators of water quality and transmit the data wirelessly. This allows us to continuously monitor the water quality and build a big enough data set to be able to use a more data driven approach to predicting changes  in water quality. However, as with any sensor system, the quality of the data can vary in time due to problems with the sensors, incorrect calibration or unforeseen issues. Real-time data quality control is crucial to prevent unsound decisions due to faulty data.</p><p>This contribution will give a general overview of the network and it’s specifications, but mainly focus on the implementation of the data stream as well as methods that are implemented to guarantee good data quality. More specifically the architecture and setup of a real-time data quality control system is described. Which will add quality control flags to measurements.  This system is  integrated with the NGSI API introduced by FIWARE, which forces us to make specific design decisions to acommodate to the NGSI API.</p>

Students Monitoring Coastal and Inland Waters With the Basic Observation Buoy (BOB)

2012 ◽  
Vol 46 (2) ◽  
pp. 56-64 ◽  
Author(s):  
Lisa G. Adams ◽  
Douglas R. Levin ◽  
Lundie Spence
Keyword(s):  
Water Quality ◽  
New England ◽  
Real Time ◽  
Scientific Discovery ◽  
Water Quality Monitoring ◽  
Inland Waters ◽  
Time Data ◽  
Link Type ◽  
Ocean Science ◽  
Real Time Data

AbstractThe Basic Observation Buoy (BOB), a student-designed and built monitoring device, provides opportunities for scientific discovery through monitoring and data collection. BOB is a scaled-down buoy fitted with sensors that monitor aquatic and atmospheric parameters in protected coastal and inland waters. Water quality parameters, like temperature, pH, dissolved oxygen, and conductivity, are measured continuously to provide real-time data. Depending on the sensor type, information can be used for education or research purposes. The BOB program was initiated in the Chesapeake Bay region by Levin and expanded to the southeast U.S. coast in 2008, via a series of three workshops funded by the Southeast Coastal Ocean Observing Regional Association (SECOORA) and the Centers for Ocean Science Education Excellence-Southeast (COSEE SE) and led by Spence (Spence et al., 2009) and Adams (Adams et al., 2010). These workshops assisted with the introduction and establishment of new programs in New England and the Great Lakes. BOB is a tool that provides science, technology, engineering, and mathematics (STEM) exploration in real-world situations for middle school to university students. Expected benefits include engagement of students in the scientific process, water quality monitoring experiences, analysis and sharing of real-time data, and the opportunity to contribute their findings to other coastal monitoring databases. Students can upload their data to an online portal supported by SECOORA and housed at the University of North Carolina-Wilmington (<ext-link ext-link-type="uri" href="http://cormp2.das.uncw.edu/dev/">http://cormp2.das.uncw.edu/dev/</ext-link>) or the National Geographic Fieldscope™ program (<ext-link ext-link-type="uri" href="http://chesapeake.fieldscope.org/">http://chesapeake.fieldscope.org/</ext-link>).

Trends and Technologies in Big Data Processing

2020 ◽  
pp. 17-42
Author(s):  
Amitava Choudhury ◽  
Kalpana Rangra
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Internet Of Things ◽  
Data Processing ◽  
Real Time ◽  
Computing Technology ◽  
Time Data ◽  
Big Data Processing ◽  
Real Time Data ◽  
Real Time Data Processing

Data type and amount in human society is growing at an amazing speed, which is caused by emerging new services such as cloud computing, internet of things, and location-based services. The era of big data has arrived. As data has been a fundamental resource, how to manage and utilize big data better has attracted much attention. Especially with the development of the internet of things, how to process a large amount of real-time data has become a great challenge in research and applications. Recently, cloud computing technology has attracted much attention to high performance, but how to use cloud computing technology for large-scale real-time data processing has not been studied. In this chapter, various big data processing techniques are discussed.

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

scholarly journals Water Quality Assessment Klang River Water Treatment Plants

2018 ◽  
Vol 7 (4.35) ◽  
pp. 639
Author(s):  
HA Mohiyaden ◽  
LM Sidek ◽  
G Hayder ◽  
MN Noh
Keyword(s):  
Water Quality ◽  
Water Treatment ◽  
Quality Assessment ◽  
River Water ◽  
Treatment Plant ◽  
Water Quality Assessment ◽  
Water Quality Monitoring ◽  
Quality Standard ◽  
Pollutant Removal ◽  
Unit Process

The quality of Klang river water is deteriorating dramatically since it is in urban places every day and become one of the major problems.  Therefore, the Malaysian government had initiated one river cleaning project named River of Life (ROL) project. This project is for rehabilitating and restoring the Klang river. A series of river water treatment plant (RWTP)s have been operated in Klang river catchment since 2014. Six RWTPs station has been monitored up to eight stations until presents. Eight parameters consisting of physio-chemical types and biological types have been recorded. RWTP effluent discharges are targeted to achieve Malaysia Interim National Water Quality Standard (INWQS) under Class II B. Since previous RWTP performance only emphasized on local river pollutants and certain conditions, this paper will investigate the effectiveness of full-scale RWTP unit process for river condition. Water quality assessment are involved which are consist of effluent water quality monitoring and pollutant removal efficiency. Most of the major pollutants able to be reduced by more than 50% reduction. Although BOD and AN still not able to achieve standard range gazetted by INWQS Class IIB, there is an improvement of river water quality at Klang River by using IFAS technology adopted in the RWTP system.

A Block Chain Approach for Privacy Preserving and Data Security in Internet of Things

2020 ◽  
Vol 17 (9) ◽  
pp. 3979-3982
Author(s):  
N. Harish Kumar ◽  
G. Deepak
Keyword(s):  
Internet Of Things ◽  
Real Time ◽  
Data Security ◽  
Cloud Storage ◽  
Storage System ◽  
Information Storage ◽  
Entire System ◽  
Time Data ◽  
Real Time Data ◽  
Block Chain

Internet of Things has been increasing its usage and recognition in vast sectors like Defence, Business, Industries, and Hospitals. The data disruption is strictly unacceptable in a number of these sectors because it could end up in serious Loss or Damages to the entire system. As of now, IOT is using a central cloud storage system for information storage and transactions. However, some examples already verified that Central cloud storage information might be hacked and changed by the specialists. This paper presents an IoT system having localized block chain storage which works on real time data and manipulates with narrowness of data interruption and modification and its recovery.

scholarly journals System-Aware Smart Network Management for Nano-Enriched Water Quality Monitoring

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
B. Mokhtar ◽  
M. Azab ◽  
N. Shehata ◽  
M. Rizk
Keyword(s):  
Water Quality ◽  
Network Management ◽  
Real Time ◽  
Learning Algorithm ◽  
Rule Learning ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
System Control ◽  
Test Bed ◽  
Time Data

This paper presents a comprehensive water quality monitoring system that employs a smart network management, nano-enriched sensing framework, and intelligent and efficient data analysis and forwarding protocols for smart and system-aware decision making. The presented system comprises two main subsystems, a data sensing and forwarding subsystem (DSFS), and Operation Management Subsystem (OMS). The OMS operates based on real-time learned patterns and rules of system operations projected from the DSFS to manage the entire network of sensors. The main tasks of OMS are to enable real-time data visualization, managed system control, and secure system operation. The DSFS employs a Hybrid Intelligence (HI) scheme which is proposed through integrating an association rule learning algorithm withfuzzylogic and weighted decision trees. The DSFS operation is based on profiling and registering raw data readings, generated from a set of optical nanosensors, as profiles of attribute-value pairs. As a case study, we evaluate our implemented test bed via simulation scenarios in a water quality monitoring framework. The monitoring processes are simulated based on measuring the percentage of dissolved oxygen and potential hydrogen (PH) in fresh water. Simulation results show the efficiency of the proposed HI-based methodology at learning different water quality classes.

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