## 5 Experimental Results and Discussion
SUMO created the vehicle-movement patterns needed by NS3 to evaluate the effectiveness of the proposed ACO-PT routing protocol. The sensor node linked to each vehicle had an initial energy of 5 joules. The simulation aimed to reduce energy consumption and improve data delivery for vehicles by simulating different realistic conditions, such as movement and static conditions. The test findings were consistent with real-world experimental outcomes and revealed that more energy was required when forwarding packets through multiple hops; this situation was exacerbated when the packet size was larger. Each sensor node was configured with a bandwidth of 100 kbps/s and an electricity consumption of 18 milliwatts. The sensor nodes connected to the vehicles were able to broadcast data at a power level ranging from -18 to 15 dBm. The simulation was run for a total of 10 min, with 20 s pauses, and a total of 12 simulation runs were included in the analysis. The simulation parameters are summarized in Table 2.  
To conduct the experiment, the following state-of-the-art dynamic vehicle scenario was generated:  
Testing scenario: The dynamic vehicle scenario was employed, which demonstrated the traffic-participant behavior that was dynamically included in the simulation. The dynamic vehicle simulation simultaneously showed how a cooperative and autonomous vehicle behaves, including how its sensors are set up and how it drives. The conduct of the surrounding traffic participants was based on the status of the autonomous vehicle in the traffic simulation. A dynamic traffic environment was created that could respond to the driving behavior of the vehicle. Dynamic coupling guaranteed that cooperative and autonomous vehicles could operate in traffic. Every participant in a traffic flow had a changeable driver model. To prevent significant step changes in the cooperative and autonomous sensor perception of the vehicle, this prediction was needed. According to the simulation results, the pause time gap in the simulation environment was sufficiently short for the results of the increased motion prediction to not significantly affect sensor perception. Additionally, \begin{table}
\begin{tabular}{l l} \hline \hline
**Name of Parameter** & **Description** \\ \hline Simulator & NS-3.37 simulator \\ Maximum number of vehicles & 400 \\ Transmission range & 45 m \\ Initial energy of the sensor node attached to vehicle & 5 Joules \\ Bandwidth of sensor node & 100 Kb/s \\ Size of simulated network & 900 \(\times\) 900 square meters \\ Pause time & 20 s \\ Simulation time & 10 min \\ Data-packet size & 512 bytes \\ Data frame & 256 bytes \\ Power consumption & 18 mW \\ Power intensity & \(-\)18–15 dBm \\ MAC protocol & BN-MAC \\ Tested algorithm & ACO-PT \\ Competing algorithms & ACO-FL, RACO and AODV-ACO \\ Mobility model & Manhattan mobility model \\ Mobility & 0–10 m/s \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Summarized simulation parameters and description. this tendency is common for environmental modeling. It is customary to apply motion models during the actual sensor-fusion process, which creates a new perspective of the surrounding world. In the dynamic vehicle simulation, the simulated environment of the autonomous vehicle and its sensor models interacted. This paper does not delve into detail on how to improve cooperative simulations. Importantly, the infrastructure may be expanded in terms of collaboration simulations. However, the simulation environment of NS-3 can be increased so that it can simulate many cooperative and autonomous vehicles at once. The ability to assess aspects such as cooperative merging, traffic distribution, overtaking, etc., can be made possible by this capacity. The digital features of the twin are programmed and incorporated into the simulation environment to make it complete. It is crucial that the vehicle model accurately depict the realistic environment of the vehicles.