CRISISSYNC: Smart Disaster Management System

Abstract

This paper presents CRISISSYNC as a solution to overcome the limitations of the traditional disaster management system. This presents the disadvantages of centralized command structures that depend heavily on uninterrupted communication and infrastructure. Due to this, incidents like delayed response can occur, resulting in loss of many lives. We propose a decentralized and intelligent disaster management framework that transforms traditional crisis handling into an adaptive, self-healing process. It integrates five innovations, which are a Predictive Digital Twin for proactive forecasting, a Crowdsourced Sense-Making AI for trusted real-time insights, a Trustful Mesh Network for resilient communication, an Autonomous Swarm Intelligence for collective decision-making, and a Dynamic Task Marketplace for optimal resource allocation. By combining all these innovations, we remove the risk of a single point of failure and move towards a Smart Disaster Management System.

Introduction

Traditional disaster management systems, relying on hierarchical command structures and sequential decision-making, often suffer from slow responses and inefficiencies during large-scale disasters. CRISISSYNC is a proposed Smart Disaster Management System that integrates emerging technologies—AI, IoT, blockchain, mesh networks, swarm intelligence, and digital twins—to create a decentralized, autonomous, and resilient ecosystem capable of functioning even under severe disruptions. The system combines predictive digital twins for forecasting, crowdsourced AI for real-time insights, trustful mesh networks for communication, autonomous swarm intelligence for decision-making, and dynamic task marketplaces for resource allocation.

Literature Review & Research Gaps:
Previous studies have explored mobile crowdsensing, UAV-based communications, blockchain authentication, and digital twin modeling for disaster management. While these technologies enhance prediction, communication, and security, existing systems often operate in isolation, lack decentralized coordination, and struggle with scalability, energy efficiency, and real-time adaptive learning. CRISISSYNC addresses these gaps by integrating these technologies into a single self-healing, trust-driven, and collaborative framework.

Methodology & Architecture:
The CRISISSYNC system is structured into multiple layers:

1. IoT Layer: Collects real-time environmental and infrastructure data via sensors and drones.

2. AI Layer: Performs predictive modeling, anomaly detection, and prioritization of actions.

3. Blockchain Layer: Secures decentralized communications.

4. Swarm Intelligence Layer: Enables autonomous collective decision-making.

5. Mesh Network Layer: Maintains peer-to-peer connectivity during infrastructure failures.

6. Digital Twin Module: Simulates disaster scenarios for planning and task optimization.

7. Cloud & Edge Computing Layer: Provides scalable, real-time data processing.

By combining these layers, CRISISSYNC transforms disaster management from reactive to proactive, autonomous, and resilient, enabling real-time coordination among devices, agents, and citizens.

Conclusion

The CRISISSYNC framework, which we proposed, introduced a major shift in disaster management. The main objective or idea lies in getting away from systems that have centralized commands towards decentralized systems. We have integrated various available technologies like Crowdsourced Sense-Making AI for gaining trusted real-time insights, a Trustful Mesh Network for flexible communication, an Autonomous Swarm Intelligence for collective decision-making, and a Dynamic Task Marketplace for maximum resource allocation. The integration of all of these technologies ensures faster prediction, trusted data exchange, and autonomous task execution without completely relying on a single point of control. The uniqueness and novelty of our idea lie in integrating all these technologies to form a disaster management system.

References

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Copyright

Copyright © 2025 Mohammed Saad Rizwan Dalal, Tamboli Aftab Ilahi, Shital Patil. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.