Enhancing Disaster Preparedness Through Quantum-Resilient Cloud Communication Networks

Herat Joshi

Chirag I Patel

R. Deepti, Mariam Fatima, Thakur Monika Singh +1 more

Shugufta Fatima, C. Kishor Kumar Reddy, Anindya Nag +1 more

Ying‐Yi Hong, Daryll John Medina, Yun-Yuan Wang

This study investigates the potential of quantum machine learning to enhance the accuracy of typhoon forecasting, with a particular emphasis on its implications for photovoltaic (PV) and wind turbine systems in the context of extreme weather preparedness. A novel approach is proposed, integrating a quantum layer into a multi-model Long Short-Term Memory (LSTM) network architecture. Implemented using CUDA-Q, the quantum layer leverages quantum phenomena to process typhoon-related data more effectively. The proposed quantum-enhanced LSTM model is evaluated on a large dataset of typhoon observations. Results demonstrate that the model achieves promising accuracy in both typhoon track forecasting—yielding mean absolute errors (MAE) of 0.0188 and 0.0307 for latitude and longitude, respectively—and intensity forecasting, with MAEs of 0.0233 for wind speed, 0.0449 for wind direction, and 0.0022 for the 50-knot wind radius. This research contributes to the growing field of quantum machine learning and its practical applications, paving the way for future advancements in typhoon prediction and disaster resilience, particularly in optimizing the operation and robustness of PV and wind turbine systems in typhoon-prone regions.

Mahmoud AlJamal, Bashar Khassawneh, Ayoub Alsarhan +4 more

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience.