AI Integrated Cloud-based Smart Child Rescue System for Open Bore Well Safety

Abstract

Open bore wells pose significant safety risks, especially to children, with many incidents leading to life threatening situations from accidental falls. Traditional rescue methods are often slow, complex, and lack real-time monitoring, which delays decisions and increases danger to victims. To address these issues, this research introduces a smart, cloud-based rescue management system to improve the efficiency and effectiveness of borehole rescues. The system uses sensor-based monitoring and IoT technology for rapid detection and response. A Passive Infrared (PIR) sensor detects movement and confirms the presence of a trapped person, while a gas sensor monitors for hazardous gases. When a victim is detected, a NodeMCU microcontroller processes the data and automatically activates an air pump to maintain safe oxygen levels. A robotic arm with a mechanical gripper assists in the physical rescue. All components connect to a cloud-based IoT platform, enabling real-time data transmission, remote monitoring, and coordinated control by emergency teams. This connectivity improves situational awareness and supports faster, more informed decisions during rescues. The study shows that integrating sensor technology, automation, and cloud-based communication can reduce response times and increase rescue success rates. This research aims to provide a safer, more reliable, and more advanced solution for borewell rescues, ultimately reducing fatalities and improving emergency response outcomes.