ARGUS: Revolutionizing Emergency Disaster Response Using AI and IOT

Authors

  • Usha Dhankar HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author
  • Rahul Dahiya HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author
  • Snehit Pandey HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author
  • Nishant HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author
  • Chandragupt Seni HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author
  • Nishant Gupta HMR Institute of Technology and Management GSSIPU, Delhi 110036 Author

DOI:

https://doi.org/10.21467/proceedings.7.6.7

Keywords:

Disaster management, AI-enabled prediction, IoT integration

Abstract

Disasters caused by nature or human activities often result in severe damage to lives and infrastructure Emergency response systems currently face problems like delayed actions poor coordination and limited communication Project Elysium ARGUS is proposed as a unified AI powered disaster management platform The system uses machine learning to predict disaster risks and IoT sensors to collect real time environmental data It facilitates immediate SOS alerts and manages volunteers through a cloud based dashboard To operate during network outages ARGUS plans to use Bluetooth Mesh and peer to peer technologies Results from simulated scenarios suggest potential improvements in response times prediction accuracy and community coordination ARGUS offers a scalable decentralized and intelligent approach to modern disaster response.

References

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Published

2025-11-21

Issue

Vol. 7 No. 6 (2025): Proceedings of the 3rd International Conference on Artificial Intelligence, Machine Learning and Cybersecurity

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

How to Cite

[1]
U. Dhankar, R. Dahiya, S. Pandey, Nishant, C. Seni, and N. Gupta, “ARGUS: Revolutionizing Emergency Disaster Response Using AI and IOT”, AIJR Proc., vol. 7, no. 6, pp. 47–56, Nov. 2025, doi: 10.21467/proceedings.7.6.7.