IoT Based Borewell Child Rescue System
Authors
Associate Professor, Department of Computer Science, Maharani’s Science College for Women (Autonomous), Mysore, Karnataka (India)
Associate Professor and Head, Department of BCA, Government College for Women (Autonomous), Mandya, Karnataka (India)
Associate Professor, Department of Computer Science, Government First Grade College, Nanganagud, Karnataka (India)
Article Information
DOI: 10.51244/IJRSI.2026.1304000233
Subject Category: Computer Science
Volume/Issue: 13/4 | Page No: 2720-2725
Publication Timeline
Submitted: 2026-04-20
Accepted: 2026-04-26
Published: 2026-05-18
Abstract
Borewell accidents involving children remain a critical safety concern due to the confined space, poor visibility, and delays in initiating effective rescue operations. Traditional rescue techniques are often slow, hazardous, and heavily dependent on manual effort, which contributes to low survival outcomes. To overcome these challenges, this paper introduces an IoT enabled borewell child rescue system featuring a compact robotic device equipped with real time monitoring and remote operation capabilities. The system incorporates a microcontroller-based control unit, sensors for tracking depth and position, a live streaming camera for visual assessment, and a motorized rescue arm designed to safely retrieve the trapped child. Wireless communication supports instant control and continuous feedback, minimizing human risk and significantly reducing rescue time. Experimental evaluations show improved precision, quicker response, and enhanced safety compared to traditional methods, demonstrating the system’s potential for reliable and efficient borewell rescue operations.
Keywords
Borewell Rescue Robot, IoT Enabled Child Rescue System
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References
1. A. Kumar, R. Singh, and P. Mehta, “Design challenges in borewell child rescue operations: A review,” International Journal of Engineering Research, vol. 7, no. 3, pp. 112– 117, 2020. [Google Scholar] [Crossref]
2. S. Radhakrishnan and M. Babu, “Robotic arm–based borewell rescue system with live monitoring,” International Journal of Advanced Robotic Systems, vol. 9, no. 2, pp. 45–51, 2019. [Google Scholar] [Crossref]
3. M. Praveen Kumar, K. Rakesh, and A. R. Patil, “Toxic gas detection and monitoring for borewell rescue robots,” IEEE Sensors Journal, vol. 21, no. 5, pp. 6231–6237, 2021. [Google Scholar] [Crossref]
4. G. Shankar and A. Joshi, “A servo-controlled robotic mechanism for confined-space rescue,” International Conference on Robotics and Automation (ICRA), pp. 354–359, 2020. [Google Scholar] [Crossref]
5. N. S. Patil, R. Kulkarni, and S. Shah, “IoT-based monitoring system for borewell rescue operations using NodeMCU,” IEEE International Conference on IoT and Applications, pp. 101–106, 2021. [Google Scholar] [Crossref]
6. H. Kumar and D. R., “Stability enhancement mechanisms in vertical robotic systems for rescue applications,” International Journal of Mechatronics, vol. 12, no. 4, pp. 211–218, 2019. [Google Scholar] [Crossref]
7. P. Sree Lakshmi, M. Rao, and S. Devi, “A cost-effective sensor-integrated robotic model for borewell rescue operations,” International Journal of Embedded Systems and Applications, vol. 8, no. 1, pp. 27–34, 2020. [Google Scholar] [Crossref]
8. M. P. Mohanraj, “Dual-camera based semi-autonomous robot for borewell victim monitoring,” IEEE Conference on Intelligent Systems, pp. 287–292, 2019. [Google Scholar] [Crossref]
9. A. N. Amrutha and K. Rakshitha, “An IoT-enabled autonomous borewell rescue robot using ESP8266,” International Conference on Smart Technologies, pp. 512–518, 2021. [Google Scholar] [Crossref]
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