Dual-Stream Hybrid CNN-Based VANET Routing Protocol with Dynamic Power Control for Topological Adaptation

Vehicular ad hoc networks (VANETs) must balance reactive and proactive routing under high mobility and changing vehicle density. Most hybrid protocols switch modes using fixed scalar thresholds, so they cannot see the spatial shape of the network around each vehicle. This work presents a lightweight version of the Deep Hybrid Routing Protocol (Deep-DHRP) built on a Dual-Stream Hybrid Convolutional Neural Network (H-CNN). One stream reads a 40 × 40 binary occupancy grid of the 1000 m × 1000 m area around the vehicle, and a second stream reads a short scalar vector (speed, distance, density). The two-stream design keeps the model small enough to run on vehicle On-Board Units (OBUs). Across an independent test sets of (84,239 samples), drawn from 80/20 train/test split, where training sets consists of 336,956 training samples, the H-CNN classifier reaches a mean accuracy of 98.06% and a weighted F1 score of 0.98 over four action classes, while the trained model stays compact at 12.93 MB. In Network Simulator (NS-3) tests from 50 to 500 nodes, Deep-DHRP keeps an average Packet Delivery Ratio (PDR) of 97.33% in sparse networks (50 nodes), peaks at 99.62% at medium density (250 nodes), and still holds 91.85% under heavy saturation (500 nodes). Latency remains strictly bounded between 3.64 ms and 15.94 ms, while throughput scales up to 0.408 Mbps with minimal overhead (1 to 27) and stable energy efficiency (96.29% to 97.11%).