An Analytical Study of Secure Drone Communication Models from a Statistical Perspective

Abstract

Drone communications are resource-intensive, limiting maintenance and reconfiguration calculations. Security protocols are frequently simpler, which enhances attack chance in high-node-density networks. To address this problem, researchers have created several low-complexity high-security methods that can neutralise many assaults. Blockchains, PuFs, Distributed Ledgers, etc. enable immutable data security and distributed computing with transparency and traceability. This paper discusses such strategies' functional intricacies, application-specific benefits, deployment-specific traits, and context-specific future scopes. This debate will help researchers choose security models for functionality-specific use cases. Bioinspired models like GA, PSO, and others may perform well in real-time network environments when paired with Q-Learning. This paper analyses examined models based on security, scalability, delay of operation, energy consumption, and deployment cost criteria to help readers choose performance-specific models for diverse circumstances. A unique Drone Security Rank Metric (DSRM) that incorporates all these criteria is also evaluated in this work to help readers select solutions with stronger security, reduced complexity, low latency, low energy, and high scalability in real-time use scenarios.