Energy Optimization in Dual-RIS UAV-Aided MEC-Enabled Internet of Vehicles

Area of Science:

• Wireless communication networks
• Mobile edge computing (MEC)
• Internet of Vehicles (IoV)

Background:

• Vehicular propagation environments pose challenges for computation offloading in Internet of Vehicles (IoV) networks.
• Mobile edge computing (MEC) is crucial for enabling advanced IoV services.
• Efficient energy management is vital for resource-constrained vehicles and roadside units (RSUs).

Purpose of the Study:

• To propose a novel computation offloading framework for IoV networks.
• To enhance communication reliability and efficiency in vehicular environments.
• To minimize the weighted total energy consumption (WTEC) in a UAV-aided MEC system.

Main Methods:

• A UAV-aided network architecture with an aerial RSU (ARSU) and ground RSU (GRSU).
• Deployment of dual reconfigurable intelligent surface (RIS) units to enhance wireless communication.
• Consideration of phase errors in RIS units for practical mobile scenarios.
• An optimization approach to minimize WTEC subject to constraints on power, timeslots, and task allocation.

Main Results:

• The proposed framework effectively manages computation offloading in complex vehicular environments.
• The dual-RIS configuration with phase error consideration improves communication performance.
• The optimization approach successfully minimizes weighted total energy consumption.
• Numerical calculations validate the efficacy of the proposed dual-RIS-assisted wireless transmission.

Conclusions:

• The developed framework offers a viable solution for computation offloading in IoV networks.
• UAVs and RIS technology can significantly enhance MEC performance in vehicular settings.
• The energy optimization strategy is effective for sustainable IoV operations.