Scalable and Resilient Autonomous Drone Swarm Framework for Secure Operations in Threatened Environments

Abstract

This research introduces a novel framework for autonomous drone swarms, addressing critical challenges in physical-cyber security by integrating advanced computational models, decentralized swarm intelligence, and robust cryptographic protocols. The work is motivated by the increasing reliance on drone swarms for securing critical infrastructure, disaster response, and surveillance, where hybrid physical and cyber threats present significant risks. The study proposes bio-inspired algorithms for adaptive swarm coordination, physics-informed neural networks for real-time collision avoidance, and quantum-inspired optimization models for resource-aware task allocation, further fortified by lattice-based cryptographic protocols to counter quantum-era adversarial threats. The experimental evaluation, conducted through high-fidelity simulations and physical deployments, demonstrates the system’s robustness in mitigating threats, achieving high collision avoidance accuracy, and maintaining communication integrity in diverse scenarios. Results show scalability with up to 100 drones in simulations and 80 drones in physical tests, highlighting bandwidth as a key area for refinement. The findings advance the field by offering a multi-layered security and coordination framework applicable to sensitive real-world settings. This study provides a foundation for enhancing operational reliability in swarm systems and opens avenues for future work in communication optimization, energy modeling, and three-dimensional navigation for complex environments.