Design and development of a portable low-cost COTS-based water quality monitoring system

Sukaridhoto, Sritrusta; Sudibyo, Rahardhita Widyatra; Sarinastiti, Widi; Dharmawan, Rizky; Sasono, Atit; Saputra, Ahmad Andika; Sasaki, Satoshi

doi:10.1109/isitia.2016.7828734

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Other OS Atlas non-EC water quality sensors-including temperature, pH, DO, and ORP sensors-have often been integrated into OS water quality monitoring systems [18,58,59]. Relatively consistent results across studies have shown that non-EC Atlas sensors also perform as well as or better than commercial options.…”

Section: Plos Onementioning

confidence: 99%

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Stegen

Kaufman

et al. 2023

PLoS ONE

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Variation in the electrical conductivity (EC) of water can reveal environmental disturbance and natural dynamics, including factors such as anthropogenic salinization. Broader application of open source (OS) EC sensors could provide an inexpensive method to measure water quality. While studies show that other water quality parameters can be robustly measured with sensors, a similar effort is needed to evaluate the performance of OS EC sensors. To address this need, we evaluated the accuracy (mean error, %) and precision (sample standard deviation) of OS EC sensors in the laboratory via comparison to EC calibration standards using three different OS and OS/commercial-hybrid (OS/C) EC sensors and data logger configurations and two commercial (C) EC sensors and data logger configurations. We also evaluated the effect of cable length (7.5 m and 30 m) and sensor calibration on OS sensor accuracy and precision. We found a significant difference between OS sensor mean accuracy (3.08%) and all other sensors combined (9.23%). Our study also found that EC sensor precision decreased across all sensor configurations with increasing calibration standard EC. There was also a significant difference between OS sensor mean precision (2.85 μS/cm) and the mean precision of all other sensors combined (9.12 μS/cm). Cable length did not affect OS sensor precision. Furthermore, our results suggest that future research should include evaluating how performance is impacted by combining OS sensors with commercial data loggers as this study found significantly decreased performance in OS/commercial-hybrid sensor configurations. To increase confidence in the reliability of OS sensor data, more studies such as ours are needed to further quantify OS sensor performance in terms of accuracy and precision across different settings and OS sensor and data collection platform configurations.

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Section: Plos Onementioning

confidence: 99%

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Stegen

Kaufman

et al. 2023

PLoS ONE

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“…There are several studies on real-time environmental monitoring used cloud computing that implements with big data concept, one of which is Smart Environment Monitoring and Analytics in Real-time System (SEMAR) [4][5][6][7][8]. SEMAR is used to detect river water quality using portable devices integrated with water quality sensors [4] and also ROV (small robot submarine) that are installed with water quality sensors [5], [6]. This is an alternative solution to assist governments in monitoring water environments in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring

Panduman

Albaab

Besari

et al. 2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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Urbanization and fast-growing industries causing air quality in urban areas to be bad and even tend to be dangerous. In addition, the largest percentage of energy emissions come from the transportation sector, specifically on road transportation. Therefore, the need for a quality detection system that is capable of distributing and displaying large data information in real-time cannot be resolved by the system currently used by the government. This research offers a solution to the implementation of data abstraction in cloud computing which is built using the concept microservice architecture and integrated with mobile-based sensors to detect air quality in real-time. This solution consists of integrated cloud computing services using Smart Environment Monitoring and Analytical in Real-time (SEMAR) and Vehicles as Mobile Sensor Networks (VaaMSN) to detecting air quality. SEMAR was built with microservice references consist of data abstraction, communication, data analytical with business analytics proccess, data storage with Big data service and also real-time visualization in maps, chart, and table through dasboard website. Through the experiments that we did show that the microservice of data abstraction layer can be installed at the SEMAR stage indicating that the average delay in sending information is around 0.09 ms (90μs), this indicates that the system can be said to be real-time. With specific and real-time locations in data visualization, the government can use this method as an new alternative method of air quality.

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“…Some of the water quality monitoring systems have been conducted before [7]. And there is still no research on cloud system architecture with the If This Then That (IFTTT) [8] model and integrate with smart aerator dynamically for aquaculture.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of Smart Aquaculture System Based on IFTTT Model and Cloud Integration

Dzulqornain¹,

Rasyid²,

Sukaridhoto³

2018

MATEC Web Conf.

Self Cite

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The internet of things technology (IoT) is growing very rapidly. IoT implementation has been conducted in several sectors. One of them is for aquaculture. For the traditional farmers, they face problems for monitoring water quality and the way to increase the quality of the water quickly and efficiently. This paper presents a real-time monitoring and controlling system for aquaculture based on If This Then That (IFTTT) model and cloud integration. This system was composed of smart sensor module which supports modularity, smart aeration system for controlling system, local network system, cloud computing system and client visualization data. In order to monitor the water condition, we collect the data from smart sensor module. Smart sensor module consists of sensor dissolved oxygen, potential of hydrogen, water temperature and water level. The components of smart aeration system are microcontroller NodeMCU v3, relay, power supply, and propeller that can produce oxygen. The system could set the IFTTT rules for the ideal water condition for the pond in any kinds of aquaculture based on its needs through the web and android application. The experimental result shows that use IFTTT model makes the aquaculture monitoring system more customizable, expandable and dynamic.

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Design and development of a portable low-cost COTS-based water quality monitoring system

Cited by 10 publications

References 15 publications

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Laboratory evaluation of open source and commercial electrical conductivity sensor precision and accuracy: How do they compare?

Implementation of Data Abstraction Layer Using Kafka on SEMAR Platform for Air Quality Monitoring

Design and Development of Smart Aquaculture System Based on IFTTT Model and Cloud Integration

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