Child Missing Alert System for Parents in Public Places
DOI:
https://doi.org/10.15662/IJEETR.2026.0802201Keywords:
Child Safety, GPS Tracking, GSM Module, IoT, Wearable Device, Mobile Application, , Emergency Alert SystemAbstract
Child safety in crowded public areas such as malls, parks, railway stations, temples, and festivals has become a major concern in recent years. Instances of children getting lost or separated from their parents are increasing due to heavy crowds and lack of real-time monitoring. This project proposes a Child Missing Alert System for Parents in Public Places using wearable devices and wireless communication technology
The proposed system uses a microcontroller-based wearable device equipped with GPS, GSM/Wi-Fi module, and emergency alert button. The device continuously monitors the child’s location and sends real-time updates to the parent’s mobile application. If the child moves beyond a predefined safe distance, the system immediately sends an alert notification along with live location tracking. This system enhances child safety, reduces panic during emergencies, and helps parents quickly locate their children in crowded environments
References
1. Daniel Konings, Fakhrul Alam, Frazer Noble, and Edmund M-K Lai (2019). Device-free Localization Wireless RSSI: comparative practical investigation
2. Kishore Kumar Reddy. N.G, Ramakrishnan.G and Rajeshwari.K (2017). “Ensuring Fishermen Safety through a Range Based System by Trizonal Localization using Low Power RSSI”
3. Pradipta Ghosh, Jason A. Tran, and Bhaskar Krishnamachari (2019). “ARREST: A RSSI Based Approach for Mobile Sensing and Tracking of a Moving Object”
4. Y. Guo, K. Huang, N. Jiang, X. Guo, Y. Li, and G. Wang, “An exponential-rayleigh model for rssbased device-free localization and tracking,” IEEE Transactions on Mobile Computing, vol. 14, no. 3,
pp. 484–494, 2015.
5. D. Konings, N. Faulkner, F. Alam, F. Noble, and E. M. K. Lai, “The effects of interference on the rssi values of a ZigBee based indoor localization system,” in 2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2017, Conference Proceedings, pp. 1–5.
6. L. Oliveira, H. Li, L. Almeida, and T. E. Abrudan (2014). “Rssi-based relative localisation for mobile robots,”.
7. D. Konings, N. Faulkner, F. Alam, F. Noble, and E. M-K Lai, “Do rssi values reliably map to rss in a localization system?” in Workshop on Recent Trends in Telecommunications Research (RTTR). IEEE, 2017
8. I. 18305:2016, Information technology – “Real time locating systems – Test and evaluation of localization and tracking systems”, ser. Standard ISO/IEC 18305:2016. ISO/IEC JT, 2016. [Online]. Available: https://www.iso.org/standard/62090.html
9. D. Vasisht, S. Kumar, and D. Katabi (2016). “Decimeter-level localization with a single wifi access point,” .
10. Simo Särkkä, Ville V. Viikari, Miika Huusko, and Kaarle Jaakkola (2012). “Phase-Based UHF RFID Tracking With Nonlinear Kalman Filtering and Smoothing”
11. Poornima, G., & Anand, L. (2025). Medical image fusion model using CT and MRI images based on dual scale weighted fusion based residual attention network with encoder-decoder architecture. Biomedical Signal Processing and Control, 108, 107932.
12. Gopinathan, V. R. (2023). Cloud-First AI Security Architecture for Protecting Enterprise Digital Ecosystems and Financial Networks. International Journal of Research and Applied Innovations, 6(6), 10031-10039.
13. Rajasekar, M. (2024). Secure Digital Banking with Federated AI: An AWS Cloud-Based Predictive Analytics Architecture for Financial Risk Intelligence. International Journal of Research and Applied Innovations, 7(3), 10735-10740.
14. Garg, V. K., Soundappan, S. J., & Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64. https://doi.org/10.36346/sarjet.2020.v02i06.003
15. Jagadeesh, S., & Sugumar, R. (2017). A Comparative study on Artificial Bee Colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243-248.
16. Sruthi, R. S., Ananya, S., & Murugeshwari, B. (2010). Web Based Virtual Control System Laboratory and On-Line Temperature Control of Electrophoresis Equipment using LabVIEW. International Journal of Computer Applications, 975, 8887.
17. Gopinathan, V. R. (2025). Intelligent Workload Scheduling for Telecom Cloud Architecture Using Reinforcement Learning. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13244-13255.
18. Rajasekar, M., (2024). Secure Digital Banking with Federated AI: An AWS Cloud-Based Predictive Analytics Architecture for Financial Risk Intelligence. International Journal of Research and Applied Innovations, 7(3), 10735-10740.
19. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
20. Kiran, A., Rubini, P., & Kumar, S. S. (2025). Comprehensive review of privacy, utility and fairness offered by synthetic data. IEEE Access.
21. Padmapriya, V. M., Thenmozhi, K., Hemalatha, M., Thanikaiselvan, V., Lakshmi, C., Chidambaram, N., & Rengarajan, A. (2025). Secured IIoT against trust deficit-A flexi cryptic approach. Multimedia Tools and Applications, 84(9), 5625-5652.
