Algorithmic Models for UAV Swarms in Smart City Fire Containment: Challenges, Limitations, and Future Directions

Urban fire outbreaks pose significant threats to human safety and infrastructure, especially in dense smart city environments where conventional firefighting resources are limited by accessibility, response time, and risk to human personnel. This study investigates algorithmic models for Unmanned Aerial Vehicle (UAV) swarms designed for fire containment and mitigation in smart cities. We identify critical gaps in current research, highlighting the lack of holistic frameworks that combine real-time fire dynamics modeling, swarm-based optimization, and resilient communication systems. Using a comparative analysis of swarm coordination algorithms, including partitioning, formation control, and hazard-aware task allocation, we outline key procedures for implementing UAV swarm fire containment. Simulation-based case studies suggest potential for scalable, adaptive swarm responses that reduce fire spread by 20–35% under stochastic propagation conditions. However, significant challenges remain in payload constraints, cyber-physical vulnerabilities, and compliance with regulatory frameworks. The findings contribute to a roadmap for AI-driven, adaptive swarm control architectures tailored for fire containment in smart city ecosystems.