# Environment-Aware Adaptive Reinforcement Learning-Based Routing for Vehicular Ad Hoc Networks
## 5 Simulation Results and Analysis
### Simulation Results and Analysis  
#### 5.2.1 Impact of Simulation Time  
In this simulation, vehicles moved rapidly on urban streets while adhering to traffic regulations. Source nodes attempted to send data packets at fixed intervals, and the performance was observed at different time points. Figures 10-14 illustrate various performance metrics, including packet delivery rate, throughput, hop count, standardized latency, and overhead ratio. At different time points depicted in the graph, vehicles moved to different positions. When the time point reached 50, the Euclidean distance from the destination was 855 m. The time points 100, 150, 200, 250, 300, 350, and 400 correspond to 515, 706, 1158, 1288, 1015, 1081, and 1400 m, respectively. At different moments, source nodes moved to different areas, thereby representing the need for routing protocols to adapt to changing environments. Figure 10 illustrates the relationship between the packet delivery rate and simulation time. EARR comprehensively considers various environmental parameters to select the optimal next hop. Meanwhile, GPSR implements a greedy strategy throughout the entire area, where intersections and buildings may lead to signal attenuation and packet loss. CAEB employs fuzzy logic to select edge nodes, and data packets are forwarded via these edge nodes. The proper selection of edge nodes has a significant impact on the protocol's performance. As simulation time goes on, vehicles travel along the streets. Compared to the GPSR and CAEB routing protocols, our proposed EARR protocol demonstrated significant performance improvement in the packet delivery ratio (PDR). Environmental changes were evident from  
Figure 11: Throughput versus simulation time. Figure 10: Packet delivery ratio versus simulation time. the GPSR's PDR, where at the start of the simulation, source nodes gradually moved closer to the destination and then moved away again. Undoubtedly, the PDR significantly improved when the nodes were closer. The graph illustrates that EARR can adapt to the environment throughout the entire simulation process, thereby maintaining a consistently high PDR. It does not experience performance fluctuations due to environmental changes, thereby showing excellent robustness. The GPSR, relying solely on node location information without considering other environmental factors, experienced decreased performance. The CAEB struggled with selecting edge nodes that could adapt to highly dynamic vehicular flows, and it also did not take other environmental factors into account. Figure 11 illustrates the relationship between the throughput and simulation time. Throughout the entire simulation timespan, EARR consistently outperformed the other protocols in terms of throughput. The throughout remained stable and showed a slight trend  
Figure 14: Overhead ratio versus simulation time. Figure 12: Average hop count versus simulation time. Figure 13: Standardization delay versus simulation time. of increasing over time, as illustrated by the changes of the nodes' positions and the other environmental conditions. In contrast, the GPSR and CAEB were significantly impacted by environmental factors. Their throughputs improved only when the nodes were closer to the destination. Once the distance increased, the lower PDR substantially hampered the throughput performance. Our protocol took into account the relative movement between nodes. Therefore, regardless of how vehicles moved, each routing decision ensured that vehicles remained within a reliable communication distance. Figure 12 displays the average hop count at different simulation time points. The EARR algorithm exhibited an average hop count that was one to two hops higher than the other routing protocols. This occurrence is unavoidable, because the EARR protocol must select the next-best hop to forward the data packets, thereby leading to the abandonment of many unstable nodes. In contrast, the GPSR always chose the node closest to the destination without considering the node's communication environment.