Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Area of Science:

• Vehicular Networks
• Content Delivery Networks
• Network Performance Optimization

Background:

• Content-Centric Vehicular Networks (CCVNs) face challenges in distributing large content like multimedia data due to vehicle mobility.
• Vehicles may not complete content downloads within a single Road-Side Unit (RSU) coverage.
• Existing mobility-based precaching schemes often use inaccurate speed estimations.

Purpose of the Study:

• To propose an Adaptive Content Pre-caching Scheme (ACPS) for vehicular networks.
• To accurately estimate vehicle speed for optimized content precaching.
• To reduce content download delay and backhaul traffic overhead.

Main Methods:

• Developed ACPS to predict vehicle speed based on current and average speeds with an optimized adaptive value.
• Calculated precaching amount using the predicted vehicle speed.
• Incorporated a guardband area to compensate for speed prediction inaccuracies.

Main Results:

• ACPS demonstrated superior performance compared to schemes using constant current or average speeds.
• Significant reductions in content download delay were observed.
• Reduced backhaul traffic overhead was achieved.

Conclusions:

• ACPS effectively addresses content download limitations in CCVNs by accurately predicting vehicle speed.
• The proposed scheme offers a practical solution for enhancing content delivery efficiency in vehicular environments.
• ACPS provides a notable improvement in network performance metrics.