Adaptive Multi-Layer Security for UAV Video Streaming: A Chaotic Blockchain Key Scheduler with MDP-Driven Reinforcement Learning

Abstract

Our adaptive multi-layer security lets UAVs communicate live video. This technique improves key scheduling over time by using MDP, chaotic maps, and blockchain technology. The bulk of essential management frameworks use legislation or centralized trust systems. In UAV networks, safe and fast video transmission is in demand, thus we must match encryption strength with operation speed. Reinforcement learning can optimize key generation and distribution. Monitor network trust scores, chaotic entropy, and blockchain update timestamps in MDP state space, and automatically remembered action refresh times in state space. The system may evolve with the network since the reward function handles unpredictability, timeliness, and trustworthiness. The system's adaptability allows this. Non-linear chaotic map framework parameter modifications can be made by using blockchain-based trust signals and entropy measurements. Removal of repeating patterns does not weaken the cryptography technique. A transformer-encoded deep Q-network produces optimal policies in the presented manner. An Old Fault Through Tolerant blockchain consensus, important blockchain modifications may be checked. The experiment shows that GPU-powered chaotic generators can use Hyperledger Fabric to reach refresh rates below one second. In practice, UAV network dynamics prove this. This work evaluates our defenses against replay and key compromise and advances high-throughput video streaming systems.