第32回日本情報地質学会講演会 講演要旨集 005-006頁 2021年

準リアルタイム水質観測のためのフリーオープンソースプラットフォームの開発
空白バンダラニロシャン*・田端秀行**・植田允教**・吉田大介**・ラガワンベンカテッシュ**

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Development Free and Open Source Platform for Near Real-TimeAcquisition of Water Quality Parameters
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BANDARA Niroshan*, TABATA Hideyuki**, UEDA Mitsunori**, YOSHIDA Daisuke**and RAGHAVAN Venkatesh**空白*大阪市立大学大学院創造都市研究科, Graduate School for Creative Cities, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan. E-mail: niroshansnj@gmail.com** 大阪市立大学大学工学研究科, Graduate School of Engineering, Osaka City University,
3-3-138 Sugimoto, Sumiyoshi-ku, Osaka 558-5858, Japan.

キーワード：水質監視システム，無線センサネット，オープンソースプラットフォーム
Key words : Water Quality Monitoring, Wireless Sensor Network, Open Source Platform

空1. Introduction

Access to Open Source software and low-cost sensors
have opened new opportunities for the design of real-timemonitoring systems in several application needs. Water
quality monitoring is crucial for effective environmentalmanagement. Therefore, it is necessary to design nearreal time water quality monitoring system for unattended
data acquisition (Jiang et al., 2020).
Geo-IoT platform with Android device for in-situ waterquality measuring has been developed and tested in theprevious study (Bandara et al., 2020). The attempt of thisstudy is to design an Open Source platform that isextendable and customizable for water quality monitoring.
Therefore, in this study we provide an integrated solution
for collection of water quality parameters for both humanassisted in-situ measurements and continuous
unattended monitoring.
We describe the framework of Open Source softwaretools that were implemented to collect, process, andvisualize the data received by sensor devices in near realtime. The current platform was tested for monitoring fourphysio-chemical water quality parameters namely, pH,Oxidation Reduction Potential (ORP), Electrical
Conductivity (EC) and Temperature.
The presented architecture can be exploited for various
monitoring scenarios for both surface water andgroundwater monitoring to identify trends of key
parameters and provide alerts when recorded data values
are beyond normal threshold situations.

CANDY Pi Lite+ communication board (candyline.com/portfolio/candy-pi-lite-lte-m) and IIJmio IoT SIM (www.
iijmio.jp/mit). Sensor device leverages above mentionedcomponents to develop data logging system. Connectivity
between each of the peripheral and single-board computeris illustrated in Figure 1.
In the continuous unattended monitoring scenario, thesensor device sends data directly to the ThingsBoard(thingsboard.io) platform over the internet. In the humanassisted water quality monitoring scenario, the sensor
device connects to Android phone via Bluetooth. Open
Data Kit (ODK, getodk.org) is as an interface for field datacollection. ODKSensor app is newly developed tointegrate data from water quality sensors withODKCollect data collection form.

2. Material and Methods

The sensor device comprises of a Raspberry Pi Zero W
single-board computer (www.raspberrypi.org/products/raspberry-pi-zero-w), Atlas Scientific pH, ORP, EC,
Temperature sensors and Tentacle T3 extension board(atlas-scientific.com). The data are logged locally on SDcard. Real-time data transmission is enabled using

Figure 1: Details of data logging system

3. System Design and Workflow

Overall system design consists of assembling sensor
devices with single board computer, implementing

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wireless transmission, software integration anddevelopment. The functions of system can be broadly be
divided as client and server components. The client side
for unattended continuous data acquisition consists ofmain sensor device and communication module. The
client side for human assisted data collection includes thesame sensor device without CANDY Pi Litecommunication board. Android device is connected tosensors via inbuilt Bluetooth module in Raspberry Pi Zero
W single-board computer for data transmission.
ODKSensor app is integrating sensor values intoODKCollect form fields.
At the Server side, the ThingsBoard Open Source IoT
platform is deployed for device management, datacollection, processing and visualization. The datacollection via Android mobile is supported using ODKCentral Server.
A middleware solution was deployed to facilitatecommunicationandinteroperability
betweenThingsBoard and ODK-Central. The middleware provides
as a hidden translation layer for seamless datavisualization on the Thingsboard dashboard.
PostgreSQL (www.postgresql.org) is used as a backend
database for both ThingsBoard and ODK-Central.
Integration of server side components is shown in Figure
2.
IoT Sensors

system’s behaviors and functions. The system kept incontinuous operation for 48 hours. Data received from thesensor device can be visualized on a customizable webbased dashboard. Figure 3 depicts the screenshot of theThingsboard dashboard.

Figure 3: Data visualization on ThingsBoard dashboard

Users can visualize present data or set the dashboard tovisualize past data with a given hour or date. Informationthat supports the interpretation of water quality data isvisualized as time series line charts, digital gauges andtables with timestamps in the panel. The spatial locationof data collection is shown on the map with background
OpenStreetMap layer.

5. Conclusion

Integrations

Real-Time dashboard

Users

Figure 2: Software integration and system architecture

Current sensor device is configured to collect data in atpredefined intervals for continuous monitoring scenario.
The timestamp from the sensor device and recorded
sensor readings are uploaded to the server using MQTT
(mqtt.org) messaging protocol for IoT. Data stored in thePostgreSQL database is instantaneously displayed onThingsBoard dashboard.
In the human assisted data collection scenario, the data
is gathered using ODKCollect mobile application. First,
the fields for sensor data is defined in the XLS form
(xlsform.org). The developed XLS form is uploaded toODK-Central server. Subsequently, the data collector candownload XLS form to an Android phone. The ODKSensor
app connects to sensor device via Bluetooth and populates
the ODKCollect form with the sensor readings. Data frommobile phone can be uploaded to the server individuallyor as a collection of data points once the internet isavailable. The middleware application is triggered when
ODK-Central receives data form Android device and data
is automatically displayed on ThingsBoard dashboard.

4. ResultsNumerous experiments were carried out to monitor theSurvey of available water quality monitoring solutions
reveals that existing platforms are either designed forfield data collection or to support unattended continuous
monitoring. Few systems permit multi-purpose uses
(Skarga-Bandurova et al., 2020). However, these systems
are not capable of fulfilling the requirements of completeclient-server platforms to support data management
needs. The system developed in this research provides acomprehensive, generic, and multi-purpose solution for
remote data collection needs. Moreover, the system can beeasily customized for a variety of real-time environmental
monitoring.

Reference

Bandara, N., Ueda, M. and Raghavan, V. (2020)
Development of Geo-IoT Platform for Water Quality
Monitoring. GEOINFORUM-2020. Vol. 31, pp 21-22.
Jiang, J., Tang, S., Han, D., Fu, G., Solomatine, D. andZheng, Y. (2020) A Comprehensive Review on The
Design and Optimization of Surface Water Quality
Monitoring Networks. Environmental Modelling &Software. Vol. 132, https://doi.org/10.1016/j.envsoft.
2020.104792.
Skarga-Bandurova, I., Krytska, Y., Velykzhanin, A.,
Barbaruk, L., Suvorin, O. and Shorokhov, M. (2020)
Emerging Tools for Design and Implementation ofWater Quality Monitoring Based on IoT. Complex
Systems Informatics and Modeling Quarterly. Vol. 24,
pp 1-14, https://doi.org/10.7250/csimq.2020-24.01.