Empowering drones in vehicular network through fog computing and blockchain technology.

Area of Science:

• Computer Science
• Network Engineering
• Cybersecurity

Background:

• Drones require efficient computation for time-sensitive tasks, but limited battery life necessitates energy-efficient systems.
• Existing drone network architectures lack comprehensive security and real-time processing capabilities for automated operations.
• Fog computing offers localized computational resources for drones, improving service response times.

Purpose of the Study:

• To propose a secure, reliable, and real-time drone network service architecture.
• To enhance drone operations through collaboration with fog computing and blockchain technology.
• To address the need for secure and efficient drone network service architectures.

Main Methods:

• Developed a Fog computing for the Drone with Blockchain Technology (FCDBT) model.
• Integrated blockchain for secure IoT data storage and processing.
• Implemented dynamic drone trajectory planning and optimized computation offloading algorithms.
• Evaluated blockchain consensus algorithms (PoW, PoS, DAG).

Main Results:

• The proposed architecture significantly reduced average response latency.
• Improved throughput was observed, especially when utilizing fog node resources.
• The Directed Acyclic Graph (DAG) consensus algorithm demonstrated superior performance for IoT data handling.

Conclusions:

• The FCDBT model provides a safe, reliable, and real-time solution for drone network services.
• Blockchain integration ensures secure data storage and processing in drone-fog environments.
• The proposed architecture effectively optimizes drone performance for IoT applications.