COVID-19 Vehicle Based on an Efficient Mutual Authentication Scheme for 5G-Enabled Vehicular Fog Computing

Area of Science:

• Computer Science
• Public Health
• Network Security

Background:

• The COVID-19 pandemic has severely impacted global society, with transportation systems identified as key transmission routes.
• Current infection tracking and healthcare monitoring in high-mobility transport are impractical for effective pandemic control.
• 5G-enabled vehicular fog computing offers potential for non-contact autonomous healthcare monitoring to improve road safety.

Purpose of the Study:

• To propose an efficient mutual authentication scheme for 5G-enabled vehicular fog computing to address the automotive pandemic.
• To develop a COVID-19 vehicle detection system for enhanced public health and road safety.
• To ensure privacy and security requirements are met while providing effective COVID-19 and healthcare solutions.

Main Methods:

• Development of a mutual authentication scheme utilizing a special flag (SF=0 for normal, SF=1 for COVID-19 vehicles).
• Integration of 5G vehicular fog computing for data interpretation and autonomous healthcare monitoring.
• Performance evaluation comparing communication and computation costs with existing related works.

Main Results:

• The proposed scheme effectively distinguishes between normal and COVID-19 vehicles using the special flag.
• The system satisfies essential privacy and security requirements for vehicular networks.
• Performance evaluations demonstrate superior efficiency in communication and computation costs.

Conclusions:

• The proposed COVID-19 vehicle detection scheme is efficient and secure for 5G-enabled vehicular fog computing.
• This approach offers a viable solution for pandemic control within transportation systems.
• The system enhances both road safety and autonomous healthcare monitoring capabilities.