Deep Reinforcement Learning-based Secure and Reliable Opportunistic Routing for VANETs

Key Points

• The central aim is to develop a secure and reliable routing mechanism for vehicular ad hoc networks (VANETs) using deep reinforcement learning techniques.
• Utilized deep reinforcement learning algorithms for routing decisions.
• Developed a framework for opportunistic routing in VANETs.
• Evaluated the performance based on security and reliability metrics.
• Enhanced routing efficiency with significant reductions in communication delays.
• Improved network security against potential attacks and failures.

Abstract