FraSNA-Net: traffic aware content caching vehicular social networks using fractional shuffle neuron attention network

Key Points

• The aim is to improve content caching strategies in vehicular social networks by employing traffic awareness techniques using a fractional shuffle neuron attention network.
• Developed a novel fractional shuffle neuron attention network for content caching.
• Implemented simulations in a vehicular social network environment to assess performance improvements.
• Analyzed caching efficiency against traditional methods.
• Enhanced content delivery speed by 30% compared to standard methods.
• Reduced network congestion significantly during peak traffic hours.
• Achieved a 25% higher cache hit rate, improving user experience.