Publication: Search and rescue tracking system using GPS and iOT _Leong, Min Qi
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Date
2024-07
Authors
Leong, Min Qi
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Abstract
Fishermen navigate maritime regions to maximize their catch, exposing themselves to dangers such as unpredictable weather and disconnection from shore. In
maritime environments, locating individuals can be challenging, especially during emergencies. This project introduces a Search and Rescue (SAR) system using Global Positioning System (GPS) and Internet of Things (IoT) technology, focusing on developing a wearable device with real-time tracking, heart rate monitoring, fall detection and an emergency alert system. The system integrates a NodeMCU ESP32 microcontroller, NEO-M8N GPS module for precise location tracking, XD-58C pulse sensor for heart rate monitoring, MPU-9250 motion tracking device for fall detection and SIM800L V2 GSM module for data ransmission. An emergency button allows fishermen to send distress signals, while a cancel button retracts false alerts. A buzzer notifies fishermen of sent alerts, and an OLED display shows the device's current location, signal strength, and battery level. Data from the sensors is transmitted to the Firebase cloud server and displayed on a website developed using HTML, CSS and JavaScript with Google Maps API integration to show real-time locations and provide detailed information on each fisherman. The device is designed to be worn on the upper arm, ensuring minimal interference with fishing activities while maintaining
comfort. It features a waterproof and rugged construction with an IP-24 rating, protecting against splashing water and solid objects over 2.5 mm. Performance tests have confirmed the device’s reliability. The GPS accuracy test showed 0.000071 and 0.000097 absolute errors, respectively, indicating high precision. Heart rate measurement had a mean absolute error of 7.1 bpm and a mean percentage error of 7.9%, which showed less accuracy. The GSM transmission test revealed robust performance above water but had limitations when submerged. Fall detection tests demonstrated the device’s ability to accurately identify real falls with an overall
accuracy of 77.78%. The device has an average operational duration of 2.34 hours.
Waterproof tests confirmed compliance with the IP-34 rating, with no water ingress
observed. Overall, the prototype developed demonstrates the device’s capability to
provide reliable real-time tracking and emergency response.