Deep Learning Assisted Dynamic Channel Allocation in VANETs for Road Condition Monitoring With Fiber Bragg Grating Sensors

ABSTRACT Reliable road‐condition monitoring is essential for safe and efficient transportation. This study proposes a deep learning‐based dynamic channel allocation (DLDCA) framework for Vehicular Ad Hoc Networks (VANETs) integrated with Fiber Bragg Grating (FBG) sensors to support real‐time detection of cracks, potholes, and structural defects. The framework employs a Deep Reinforcement Learning (DRL) model that continuously adapts channel assignments based on traffic density, interference, signal strength, and latency constraints, enabling efficient spectrum usage under varying network conditions. Simulation results demonstrate significant gains over static allocation approaches, including a 90% increase in packet delivery rate, 25 Mbps improvement in throughput, 65% rise in channel utilization, and a 40 packets/s reduction in congestion. The end‐to‐end delay is consistently maintained below 80 ms. These outcomes confirm that the DLDCA framework enhances communication reliability and supports proactive road maintenance in next‐generation intelligent transportation systems.