Disaster Monitoring and Intelligent Evacuation Planning System using Edge Computing and Simulation
DOI:
https://doi.org/10.15662/IJEETR.2026.0802078Keywords:
Internet of Things (IoT), Stampede Detection, Crowd Management, Vibration Sensors, Machine Learning, Decision TreeAbstract
This paper presents an Internet of Things (IoT) and machine learning-based real-time stampede detection and management system aimed at improving safety in crowded public spaces such as religious gatherings, transportation hubs, and large events. Stampede incidents often happen because there is no continuous monitoring or early warning systems in place, leading to serious injuries and loss of life. The system uses vibration sensors to monitor ground vibrations caused by crowd movement. When the detected vibration intensity goes beyond a set limit, the system automatically turns on visual and audible alerts using LED indicators and a buzzer. Additionally, servo motors lift and control access gates to manage crowd entry and exit during critical situations. Real-time vibration data, system status, and alert notifications are sent to users through a cloud-based IoT platform. Sensor data is logged periodically into a cloud-hosted database at regular intervals for centralized storage and further analysis. A Decision Tree machine learning model analyzes the historical data and classifies crowd conditions, allowing for the early identification of potential stampede situations. Experimental results show that the system effectively detects unusual crowd behavior and offers timely alerts, thereby enhancing response time and lowering the risk of stampede incidents. The system is cost-effective, scalable, and suitable for real-world crowd management applications
References
1. D. Helbing and A. Johansson, “Pedestrian, Crowd and Evacuation Dynamics,” Encyclopedi of Complexity and Systems Science, Springer, pp. 6476–6495, 2009.
2. S. Bandini, M. Manzoni, and G. Vizzari, “Crowd Behaviour Modelling: From Individual Agents to Collective Phenomena,” Journal of Artificial Societies and Social Simulation, vol. 12, no. 1, pp. 1–14, 2009.
3. M. Moussaïd, D. Helbing, and G. Theraulaz, “How Simple Rules Determine Pedestrian Behavior and Crowd Disasters,” Proceedings of the National Academy of Sciences, vol. 108, no. 17, pp. 6884–6888, 2011.
4. A. Johansson, D. Helbing, and P. K. Shukla, “Specification of the Social Force Pedestrian Model by
5. Evolutionary Adjustment to Video Tracking Data,” Advances in Complex Systems, vol. 10, no. 02, pp. 271–288, 2007.
6. S. Ali and M. Shah, “A Lagrangian Particle Dynamics Approach for Crowd Flow Segmentation and Stability Analysis,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–6, 2007.
7. V. Raghavan, A. Kumar, and S. Kumar, “IoT Based Smart Crowd Monitoring System,” International Journal of Engineering and Advanced Technology, vol. 8, no. 5, pp. 1817–1821, 2019.
8. A. Zanella, N. Bui, A. Castellani, L. Vangelista, and M. Zorzi, “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22–32, Feb. 2014.
9. J. Wang, G. Yu, and J. Wu, “Crowd Density Estimation Using Vibration Sensors,” Sensors, vol. 18, no. 5, pp. 1–15, 2018.
10. R. Kumar and M. P. Singh, “Sensor-Based Early Warning System for Crowd Disaster Management,” International Journal of Disaster Risk Reduction, vol. 27, pp. 239–247, 2018.
11. S.Madakam, R. Ramaswamy, and S. Tripathi, “Internet of Things (IoT): A Literature Review,” Journal of Computer and Communications, vol. 3, no. 5, pp. 164–173, 2015.
12. T. M. Mitchell, Machine Learning, New York, NY, USA: McGraw-Hill, 1997.
13. C.Nagarajan and M.Madheswaran - ‘Stability Analysis of Series Parallel Resonant Converter with Fuzzy Logic Controller Using State Space Techniques’- Taylor &Francis, Electric Power Components and Systems, Vol.39 (8), pp.780-793, May 2011. DOI: 10.1080/15325008.2010.541746
14. C.Nagarajan and M.Madheswaran - ‘Experimental verification and stability state space analysis of CLL-T Series Parallel Resonant Converter’ - Journal of Electrical Engineering, Vol.63 (6), pp.365-372, Dec.2012. DOI: 10.2478/v10187-012-0054-2
15. C.Nagarajan and M.Madheswaran - ‘Performance Analysis of LCL-T Resonant Converter with Fuzzy/PID Using State Space Analysis’- Springer, Electrical Engineering, Vol.93 (3), pp.167-178, September 2011. DOI 10.1007/s00202-011-0203-9
16. S.Tamilselvi, R.Prakash, C.Nagarajan,“Solar System Integrated Smart Grid Utilizing Hybrid Coot-Genetic Algorithm Optimized ANN Controller” Iranian Journal Of Science And Technology-Transactions Of Electrical Engineering, DOI10.1007/s40998-025-00917-z,2025
17. S.Tamilselvi, R.Prakash, C.Nagarajan,“ Adaptive sliding mode control of multilevel grid-connected inverters using reinforcement learning for enhanced LVRT performance” Electric Power Systems Research 253 (2026) 112428, doi.org/10.1016/j.epsr.2025.112428
18. S.Thirunavukkarasu, C. Nagarajan, 2024, “Performance Investigation on OCF and SCF study in BLDC machine using FTANN Controller," Journal of Electrical Engineering And Technology, Volume 20, pages 2675–2688, (2025), doi.org/10.1007/s42835-024-02126-w
19. C. Nagarajan, M.Madheswaran and D.Ramasubramanian- ‘Development of DSP based Robust Control Method for General Resonant Converter Topologies using Transfer Function Model’- Acta Electrotechnica et Informatica Journal , Vol.13 (2), pp.18-31,April-June.2013, DOI: 10.2478/aeei-2013-0025.
20. C.Nagarajan and M.Madheswaran - ‘DSP Based Fuzzy Controller for Series Parallel Resonant converter’- Springer, Frontiers of Electrical and Electronic Engineering, Vol. 7(4), pp. 438-446, Dec.12. DOI 10.1007/s11460-012-0212-0.
21. C.Nagarajan and M.Madheswaran - ‘Experimental Study and steady state stability analysis of CLL-T Series Parallel Resonant Converter with Fuzzy controller using State Space Analysis’- Iranian Journal of Electrical & Electronic Engineering, Vol.8 (3), pp.259-267, September 2012.
22. C.Nagarajan and M.Madheswaran, “Analysis and Simulation of LCL Series Resonant Full Bridge Converter Using PWM Technique with Load Independent Operation” has been presented in ICTES’08, a IEEE / IET International Conference organized by M.G.R.University, Chennai.Vol.no.1, pp.190-195, Dec.2007
23. Suganthi Mullainathan, Ramesh Natarajan, “An SPSS and CNN modelling based quality assessment using ceramic materials and membrane filtration techniques”, Revista Materia (Rio J.) Vol. 30, 2025, DOI: https://doi.org/10.1590/1517-7076-RMAT-2024-0721
24. M Suganthi, N Ramesh, “Treatment of water using natural zeolite as membrane filter”, Journal of Environmental Protection and Ecology, Volume 23, Issue 2, pp: 520-530,2022
25. J. Quinlan, “Induction of Decision Trees,” Machine Learning, vol. 1, no. 1, pp. 81–106, 1986.
26. F. Provost and T. Fawcett, “Data Science and Its Relationship to Big Data and Data-Driven Decision Making,” Big Data, vol. 1, no. 1, pp. 51–59, 2013.
27. A. Al-Fuqaha et al., “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications,” IEEE Communications Surveys & Tutorials, vol. 17, no. 4, pp. 2347–2376, 2015.
28. P. S. Rao and K. R. Rao, “IoT-Based Crowd Monitoring and Stampede Avoidance System,” IEEE International Conference on Smart Technologies and Management for Computing, Communication, Controls, Energy and Materials (ICSTM), pp. 214–219, 2017.
29. Vimal, V. R., & Banerjee, J. S. (2025). Integrating PSO, GA, and ACO for Optimized ECG Feature Selection and Classification of Cardiac Disorders. SGS-Engineering & Sciences, 1(5).
30. 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.
31. Mathew, A. A Secure, Trustworthy, and Regulated Framework for AI Agents in Distributed Networks.
32. Anbazhagan, K. (2025). Secure AI Enabled Enterprise Ecosystems for Fraud Prevention Compliance Automation and Real Time Analytics. International Journal of Multidisciplinary Research in Science, Engineering, Technology & Management, 1(4), 6-13.
33. Soundappan, S. J. (2026). Building Trustworthy AI: Explainability and Security in Modern Cloud-Native Data-Driven Ecosystem Platforms. International Journal of Engineering & Extended Technologies Research (IJEETR), 8(2), 570-579.
34. Sugumar, R. (2025). Cyber-Secure Cloud Architecture Integrating Network and API Controls for Risk-Aware SAP Healthcare Data Platforms. International Journal of Humanities and Information Technology, 7(4), 53-60.
35. Vimal, V. R., & Banerjee, J. S. (2025). Integrating PSO, GA, and ACO for Optimized ECG Feature Selection and Classification of Cardiac Disorders. SGS-Engineering & Sciences, 1(5).
36. Gopinathan, V. R. (2025). AI-Powered Kubernetes Orchestration for Complex Cloud-Native Workloads. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 8(6), 13215-13225.
37. Mathew, A. From Conversation to Command Execution: A Comparative Threat Modeling and Risk Analysis of OpenClaw and ChatGPT. Risk, 100(1).
38. Inbavalli, M., & Arasu, T. (2015). Efficient Analysis of Frequent Item Set Association Rule Mining Methods. International Journal of Scientific & Engineering Research, 6(4).
39. Sugumar, R. (2025). Secure and Explainable AI Systems in Cloud-Based Applications: Bridging Trust and Performance. International Journal of Engineering & Extended Technologies Research (IJEETR), 7(4), 10328-10335.
40. Rajasekar, M. (2025). Risk-Aware Generative AI and Machine Learning Frameworks for Privacy-Preserving Banking and Trade Analytics over Cloud and 5G Networks. International Journal of Computer Technology and Electronics Communication, 8(4), 11078-11086.
41. Gopalakrishnan, S., Dhinakaran, D., Raja, S. E., Raghavan, P., & Girija, M. S. (2026). Fusion-Driven Medical Image Encryption Framework with Entropy-Calibrated Control and Integrity Assurance. KSII Transactions on Internet & Information Systems, 20(2).
42. G. Vimal Raja, K. K. Sharma (2014). Analysis and Processing of Climatic data using data mining techniques. Envirogeochimica Acta 1 (8):460-467.
