Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks

Area of Science:

• Intelligent Transportation Systems
• Wireless Communication Networks
• Network Routing Protocols

Background:

• Vehicular ad hoc networks (VANETs) face routing challenges due to high vehicle mobility and complex urban environments.
• Traditional topology-based routing is unsuitable for dynamic VANETs, leading to the preference for location-based routing.
• Channel contention and urban structures complicate wireless communication in VANETs, demanding robust routing solutions.

Purpose of the Study:

• To propose a novel environment-aware adaptive reinforcement routing (EARR) protocol for VANETs.
• To enhance the reliability and efficiency of communication in intelligent transportation systems.
• To address challenges like high dynamics, shadow fading, and limited bandwidth in VANETs.

Main Methods:

• The EARR protocol utilizes periodic beacons to gather environmental data and construct a local topology.
• Reinforcement learning is applied to adaptively adjust routing decisions based on perceived metrics (speed, bandwidth, signal strength).
• The protocol optimizes next-hop selection to form suboptimal end-to-end routes and handle link interruptions.

Main Results:

• The EARR protocol demonstrated significant improvements in performance metrics compared to existing routing protocols.
• Consistent high packet-delivery rate and throughput were maintained across various simulation scenarios.
• The protocol exhibited stable performance with relatively consistent standardized latency and low overhead.

Conclusions:

• The EARR protocol effectively enhances communication reliability in dynamic VANET environments.
• Reinforcement learning integration allows for adaptive routing, improving network performance.
• EARR offers a promising solution for robust routing in intelligent transportation systems.