Implementation of IoT Platform Analytics for Monitoring Coastal Water Conditions

DOI

10.2991/978-94-6463-364-1_55How to use a DOI?

Keywords

IoT; MQTT; ELK-Stack; SVM; Water Monitoring

Abstract

In 2018, the level of damage to mangrove forests reached 5.9 million hectares or around 68.8%. As the damage increases, some action must be taken to maintain sea water quality by researching and monitoring water quality. The research can be done conventionally but will have several obstacles including research time, consistency of research time, distance traveled, and recap of research data. Therefore, will be made a platform that can handle these problems based on the Internet of Things (IoT). By utilizing IoT, monitoring can be carried out more efficiently by detecting temperature, salinity, dissolved oxygen, pH, water turbidity, and water levels using sensors. The sensor is implanted on Arduino and then sent using ESP32 to MQTT and then will be visualized using ELK-Stack and analyzed using the Water Pollution Index (IP) method which is calibrated with the Support Vector Machine (SVM) method. If an anomaly occurs, a notification will be sent to Telegram. The research of monitoring water quality and application functionality has been successfully carried out with scenarios of testing applications that are connected to devices and sensors on the coast of mangrove forests. The result of this study indicates that the sensor device can transmit data according to sea water quality conditions in real time. The data was successfully displayed in the form of tables and graphs on the website using ELK-Stack. Besides that, the testing using SVM method gives result 95% for accuracy, 94% for average precision, 95% for average recall, and 94% for average f1-score. It has been successful sending notification to user’s telegram if there were an anomaly.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Udin Harun Rasyid
AU  - Arif Basofi
AU  - Sritrusta Sukaridhoto
AU  - Yanu Adi Nugraha
PY  - 2024
DA  - 2024/02/17
TI  - Implementation of IoT Platform Analytics for Monitoring Coastal Water Conditions
BT  - Proceedings of the International Conference on Applied Science and Technology on Engineering Science 2023 (iCAST-ES 2023)
PB  - Atlantis Press
SP  - 587
EP  - 606
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-364-1_55
DO  - 10.2991/978-94-6463-364-1_55
ID  - Rasyid2024
ER  -