Author
Listed:
- Alisha Arif Patel
- Pallavi Maruti Andhale
- Riddhi Atul Jagtap
- P. J. Khamkar
Abstract
Natural disasters such as floods, earthquakes, cyclones, landslides, and wildfires often cause significant loss of life, property damage, and disruption of essential services. Traditional disaster management systems rely on centralized databases, manual reporting mechanisms, and fragmented communication channels, resulting in delayed response times, poor coordination, and limited transparency. To address these challenges, this paper proposes AEGIS (Advanced Emergency Governance & Integrated Security Network), a Blockchain-Based Disaster Management and Response Network designed to enhance emergency response operations through secure, transparent, and real-time disaster management services. The proposed system integrates Ethereum Blockchain, Smart Contracts, GIS Mapping, Socket.io, MongoDB, and Next.js technologies to provide a decentralized platform for disaster reporting, verification, rescue coordination, and resource management. Citizens can report emergencies, share location information, and receive alerts, while government agencies, rescue teams, and NGOs can coordinate relief operations through dedicated dashboards. Blockchain technology ensures immutable disaster records, secure verification, and transparent audit trails, while GIS mapping enables real-time monitoring of affected areas and rescue activities. JWT-based authentication and role-based access control enhance system security and data protection. The proposed platform improves communication, transparency, accountability, and operational efficiency during disaster situations. Experimental implementation demonstrates that AEGIS provides a scalable, secure, and reliable framework for modern disaster management and emergency response operations.
Suggested Citation
Alisha Arif Patel & Pallavi Maruti Andhale & Riddhi Atul Jagtap & P. J. Khamkar, 2026.
"Blockchain-Based Disaster Management and Response Network,"
International Journal of Scientific Research in Artificial Intelligence and Machine Learning, International Journal of Scientific Research in Artificial Intelligence and Machine Learning, vol. 2(3), pages 220-229, May.
Handle:
RePEc:jbo:ijsrml:v2:y2026:i3:id:75
DOI: 10.32628/IJSRAIML262312
Note: Article URL: https://ijsraiml.com/home/article/view/IJSRAIML262312
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