Efficient and trusted autonomous vehicle routing protocol for 6G networks with computational intelligence.

Area of Science:

• Computer Science
• Electrical Engineering
• Network Communications

Background:

• The Internet of Things (IoT) and wireless sensors are integral to real-time data collection.
• Sixth-generation (6G) mobile networks support advanced applications like Connected and Autonomous Vehicles (CAV).
• Existing autonomous vehicle routing faces challenges in data delivery and latency due to unpredictable IoT environments.

Purpose of the Study:

• To propose an efficient and trusted autonomous vehicle routing protocol utilizing 6G networks.
• To guarantee high quality of service and data coverage for CAV applications.
• To address data transmission latency and environmental uncertainties in IoT-enabled autonomous systems.

Main Methods:

• A simulated annealing optimization technique is employed for energy-efficient routing among IoT-based vehicles.
• A risk-aware security system is implemented for reliable, session-oriented communication with network edges.
• The protocol's performance is verified through simulations with varying vehicle numbers and iterations.

Main Results:

• The protocol statistically guarantees optimal routing solutions, even in challenging conditions.
• It enhances energy optimization between connected vehicles.
• The system demonstrates improved data coverage and reduced uncertainties in autonomous operations.

Conclusions:

• The proposed 6G-based routing protocol offers an efficient and secure solution for CAVs.
• It contributes to a green communication system for autonomous driving, ensuring authenticity and intelligence.
• The protocol effectively manages data delivery and minimizes latency in dynamic IoT environments.