Security-enabled optimal placement of drone-assisted intelligent transportation systems in mission-critical zones

Abstract

A massive increase in highly dynamic vehicular nodes has resulted in network instability. Owing to the heterogeneous vehicular environment requires a multi-objective solution using a meta-heuristic optimization algorithm in the event of mission-critical zones with poor signal and secured quick decision-making system. Developed a security-enabled optimal placement of Drones or unmanned aerial vehicles (UAV) in mission-critical zones aims to achieve two primary objectives: 1) Maximizing the effectiveness of the intelligent transportation system (ITS) for traffic management and ubiquitous connectivity in mission-critical zones. 2) Ensuring robust security measures to protect sensitive data and infrastructure. This approach represents a cutting-edge solution for optimizing transportation systems in high-risk environments while safeguarding against potential security threats. The pre-deployment of drones and vehicles (V_OBU) parameter occurs during the registration phase, and then the mission-critical zone (MCZ) is identified and stored. The optimal position for drones in MCZs is determined by mathematically modeling a golden eagle optimization (GEO), which is inspired by varying the speed at different stages along their spiral trajectory for cruising and hunting. Furthermore, the robustness of the sensitive data and the real identity is ensured by using a biometric-based AKA algorithm utilizing the prevalent real-or-random (ROR) model and the formal security analysis. Based on a comparison of the simulation results, the proposed SDV-GEOAKA scheme outperforms the existing system- STPTC-A2 G, IoDAV, and IMOC with 99.36 % of PDR approximately, whereas, SDV-GEOAKA has maintained a load balancing factor with 0.01 to 0.1 when the transmission range between 0 and 60. When it comes to network coverage, proposed work outperforms with 99.95 % during the transmission range of 50 mW means it uses a minimum number of drones with maximum connectivity within the coverage range and also has significantly reduced the computation overhead and an increase in anomaly detection rate.