Bore Safe: IoT-Powered Child Monitoring System for Tube Wells

Tube wells are prevalent in rural and semi-urban locations, where groundwater is the most significant source of potable and agricultural water. However, these wells present substantial hazards to minors, who may sustain life-threatening injuries or even die. So, the paper provides a child monitoring system that is tube-based and utilizes the Internet of Things (IoT), wireless connectivity, machine learning, and sensors. It will control the temperature, ultrasonic, and heart rate sensors by using an Arduino Uno. Further, all data is sent in real time through an ESP8266 Wi-Fi module. The same data can be further analyzed through the use of a Random Forest Classifier (RFC), which can come up with an accuracy of 97.1% in identifying potential hazards by using 10-fold cross-validation. Push notifications and text messages on their smartphones are sent to parents and authorities. This method seeks to give a proactive and efficient approach towards preventing incidents within the surroundings of tube wells with regards to safety among children.

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Publication Details

Published
2024-12-12
DOI
https://doi.org/10.1109/aicecs63354.2024.10957556
Primary Topic
IoT-based Smart Home Systems
Type
article
Field-Weighted Citation Impact
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Bore Safe: IoT-Powered Child Monitoring System for Tube Wells

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IoT-based Smart Home Systems
article

Bore Safe: IoT-Powered Child Monitoring System for Tube Wells

F. Emerson Solomon, T. Manoj Prasath, Vasuki Rajaguru, R. Indumathi, K. Halith
article en

Abstract

Tube wells are prevalent in rural and semi-urban locations, where groundwater is the most significant source of potable and agricultural water. However, these wells present substantial hazards to minors, who may sustain life-threatening injuries or even die. So, the paper provides a child monitoring system that is tube-based and utilizes the Internet of Things (IoT), wireless connectivity, machine learning, and sensors. It will control the temperature, ultrasonic, and heart rate sensors by using an Arduino Uno. Further, all data is sent in real time through an ESP8266 Wi-Fi module. The same data can be further analyzed through the use of a Random Forest Classifier (RFC), which can come up with an accuracy of 97.1% in identifying potential hazards by using 10-fold cross-validation. Push notifications and text messages on their smartphones are sent to parents and authorities. This method seeks to give a proactive and efficient approach towards preventing incidents within the surroundings of tube wells with regards to safety among children.

Bharath University (IN)
Openalex Percentile: Top 73%
IoT-based Smart Home Systems
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