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

 

 

 



Enhancing Intelligent Transportation Systems in Mission-Critical Zones through Security-Enabled Optimal UAV Placement using Meta-Heuristic Optimization

In recent years, the rapid increase in highly dynamic vehicular nodes has posed significant challenges to the stability and reliability of vehicular ad hoc networks (VANETs).

These challenges are particularly pronounced in heterogeneous vehicular environments, where fluctuating node density, mobility, and connectivity necessitate robust, adaptive, and multi-objective solutions.

In mission-critical zones (MCZs), where the preservation of human life, infrastructure integrity, and data confidentiality are paramount, the deployment of intelligent systems must also prioritize secure and real-time decision-making capabilities.

To address these complexities, this study proposes a novel framework, termed SDV-GEOAKA, which integrates secure drone placement and authentication mechanisms into the intelligent transportation ecosystem. This approach is centered on the optimal positioning of drones or unmanned aerial vehicles (UAVs) to facilitate enhanced traffic management, reliable communication, and fortified cybersecurity within mission-critical zones.

The SDV-GEOAKA framework is fundamentally grounded in a multi-objective optimization strategy leveraging the Golden Eagle Optimization (GEO) algorithm. The GEO algorithm is inspired by the behavioral dynamics of golden eagles, particularly their ability to modulate speed across spiral trajectories during hunting and cruising. This biologically inspired meta-heuristic is employed to compute the optimal spatial deployment of UAVs in MCZs, ensuring maximal network coverage while minimizing the number of UAVs required. This efficiency translates into cost-effective and energy-conserving network management, making it a viable solution for large-scale deployment.

One of the pivotal phases of the proposed framework is the pre-deployment stage, during which vehicle-onboarding units (VOBUs) are registered, and mission-critical zones are identified and recorded. This preparatory phase enables proactive planning and rapid mobilization of drones upon detection of critical events or signal degradation.

In parallel with optimization for UAV placement, the SDV-GEOAKA framework incorporates a rigorous security layer to protect sensitive data and prevent unauthorized access. A biometric-based Authentication and Key Agreement (AKA) mechanism is utilized, built upon the real-or-random (ROR) model. This approach ensures the authenticity of communicating entities and the integrity of transmitted information, even in the presence of potential adversaries. The biometric-based scheme further enhances security by tying access permissions to unique physiological attributes, thereby mitigating risks associated with identity spoofing and credential theft.

Simulation results underscore the superiority of the SDV-GEOAKA system over existing protocols such as STPTC-A2G, IoDAV, and IMOC. In terms of Packet Delivery Ratio (PDR), SDV-GEOAKA achieves an impressive 99.36%, which demonstrates its efficacy in maintaining communication reliability in volatile network conditions. Additionally, the proposed system exhibits a load balancing factor ranging from 0.01 to 0.1 across transmission ranges of 0 to 60 meters, reflecting efficient resource distribution across the network.

Moreover, the system demonstrates near-complete network coverage—99.95%—at a transmission power of 50 mW, indicating high efficiency in UAV usage. This translates to a greater area being covered with fewer drones, thereby optimizing both resource allocation and operational logistics. Another critical advantage lies in the reduced computational overhead and the increased anomaly detection rate, which are vital for real-time responsiveness and threat mitigation in mission-critical scenarios.

In conclusion, the SDV-GEOAKA framework presents a comprehensive and secure approach to enhancing intelligent transportation systems in high-risk environments. By integrating biologically inspired optimization with biometric security mechanisms, the proposed system not only enhances the performance and coverage of UAV-assisted networks but also establishes a robust line of defense against cyber threats. This dual emphasis on performance and security positions SDV-GEOAKA as a pioneering solution for next-generation ITS deployments in mission-critical domains.

 References

Cheema, M. A., Shehzad, M. K., Qureshi, H. K., Hassan, S. A., & Jung, H. (2020). A Drone-Aided Blockchain-Based Smart Vehicular Network

Souli, N., Karatzia, M., Georgiades, C., Kolios, P., & Ellinas, G. (2024). Mission-critical UAV swarm coordination and cooperative positioning using an integrated ROS-LoRa-based communications architecture

Cai, Y., Wei, Z., Li, R., Ng, D. W. K., & Yuan, J. (2020). Joint Trajectory and Resource Allocation Design for Energy-Efficient Secure UAV Communication Systems

 Akram, R. N., Markantonakis, K., Mayes, K., Habachi, O., Sauveron, D., Steyven, A., & Chaumette, S. (2017). Security, Privacy and Safety Evaluation of Dynamic and Static Fleets of Drones

   

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