Green SDN‑Enabled Mesh Infrastructures Energy‑Efficient Network Architectures for Smart Cities

This paper presents a novel framework for resilient AI-driven network slicing in disaster-aware 6G infrastructures. The study focuses on enhancing the reliability, adaptability, and efficiency of next-generation communication networks under extreme conditions. The proposed approach integrates artificial intelligence techniques with dynamic network slicing to enable real-time resource allocation, fault tolerance, and intelligent traffic management during disasters. It leverages advanced orchestration mechanisms to ensure service continuity and optimal performance across heterogeneous network environments. Simulation and analytical results demonstrate that the proposed model significantly improves network resilience, reduces latency, and enhances quality of service compared to traditional architectures. This work contributes to the development of robust and intelligent 6G infrastructures capable of supporting critical applications in emergency and disaster scenarios.