SSDBFAN: Scalable and Secure Cluster-Based Data Aggregation with Blockchain for Flying Ad Hoc Networks

Area of Science:

• Computer Science
• Network Security
• Artificial Intelligence

Background:

• Flying Ad Hoc Networks (FANETs) using mobile Unmanned Aerial Vehicles (UAVs) face security and privacy challenges in data aggregation due to dynamic structures and limited resources.
• Traditional cluster-based data aggregation in FANETs can compromise data privacy, necessitating advanced security measures.

Purpose of the Study:

• To propose SSDBFAN, a scalable and secure cluster-based data aggregation framework for FANETs.
• To enhance data privacy and security in FANETs through integration of novel optimization and cryptographic techniques.

Main Methods:

• Integration of the Frilled Lizard Optimization Algorithm (FLOA) for efficient Cluster Head (CH) selection.
• Implementation of blockchain technology with post-quantum cryptography (lattice-based homomorphic encryption, Chinese Remainder Theorem) for privacy-preserving aggregation.
• Utilizing a hybrid online/offline signature mechanism for secure and efficient authentication.

Main Results:

• SSDBFAN demonstrates significant improvements in communication efficiency, reduced computational cost, and enhanced network stability compared to existing schemes.
• Scalability analysis up to 500 UAV nodes shows effective control of blockchain overhead (bandwidth, latency, storage).
• FLOA outperforms existing algorithms in cluster stability, delay, and throughput.

Conclusions:

• SSDBFAN provides a scalable and security-aware solution for data aggregation in large-scale FANET environments.
• The framework effectively addresses privacy concerns and enhances the overall performance and security of FANETs.