## 6 Discussion of Results  
The proposed ACO-PT heterogeneous algorithm aims to find an efficient and optimal vehicle path by combining the best aspects of two cutting-edge algorithms: pheromone termite and ant-colony optimization. To enhance the reliability of the algorithm, an  
Figure 10: (**a**) Energy consumption of the proposed ACO-PT and contending algorithms (ACO-FL, RACO, and AODV-ACO) with a maximum of 200 vehicles, (**b**) energy consumption of the proposed ACO-PT and contending algorithms (ACO-FL, RACO, and AODV-ACO) with a maximum of 400 vehicles. automatic-vehicle-detection module was added that includes image preprocessing, contour detection, contour matching, and blob detection. Additionally, the proposed algorithm addresses congestion avoidance to improve throughput, energy consumption, and end-to-end latency. A comparative analysis of the ACO-PT approach with existing state-of-the-art algorithms such as ACO-FL, RACO, and AODV-ACO in terms of throughput, end-to-end delay, and energy consumption was conducted. The results demonstrate that the proposed ACO-PT outperformed other contending methods. The performance and differences are shown in Tables 3 and 4. The proposed ACO-PT algorithm achieved a throughput of 90.2 kbps with 200 vehicles and 78.2 kbps with 400 vehicles. Competing methods, on the other hand, achieved a lower throughput between 62.1-68.5 kbps with 200 vehicles and 50-62.1 kbps with 400 vehicles. Furthermore, the proposed ACO-PT consumed less energy and had a shorter end-to-end latency with a varying number of vehicles. The proposed ACO-PT had an end-to-end latency of 226.3 milliseconds with 200 vehicles and 250 milliseconds with 400 vehicles, as depicted in the tables below. In contrast, the competing methods caused an end-to-end delay of 262.3-363.3 milliseconds with 200 vehicles and 300.8-388.7 milliseconds with 400 vehicles. Hence, with a varying number of vehicles, the suggested ACO-PT used less energy compared to the competing algorithms. In conclusion, the experimental results demon \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Average Throughput (Kbps)** & \begin{tabular}{c} **End-to-End Delay** \\ **(Milliseconds)** \\ \end{tabular} & \begin{tabular}{c} **Energy Consumption** \\ **(Joules)** \\ \end{tabular} \\ \hline ACO-FL & \begin{tabular}{c} An improved foundation for the \\ ACO protocol based on fuzzy logic. \\ \end{tabular} & 62.1 & 388.7 \\ \hline RACO & \begin{tabular}{c} Routing using ant-colony \\ ACO protocol based on fuzzy logic. \\ \end{tabular} & 56 & 330.8 \\ \hline AODV-ACO & \
\begin{tabular}{c} AODV combined with ACO \\ (AODV-ACO) for data \\ transmission. \\ \end{tabular} & 50 & 300.8 \\ \hline ACO-PT &
\begin{tabular}{c} A novel heterogeneous algorithm \\ called ant-colony optimization with \\ pheromone termite. \\ \end{tabular} & 78.2 & 250 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative analysis of the proposed model with various state-of-the-art algorithms with a maximum of 400 vehicles. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Average Throughput (Kbps)** & \begin{tabular}{c} **End-to-End Delay** \\ **(Milliseconds)** \\ \end{tabular} & \begin{tabular}{c} **Energy Consumption** \\ **(Joules)** \\ \end{tabular} \\ \hline ACO-FL & \begin{tabular}{c} An improved foundation for the \\ ACO protocol based on fuzzy logic. \\ \end{tabular} & 68.5 & 363.3 \\ \hline RACO & \begin{tabular}{c} Routing using ant-colony \\ optimization (RACO). \\ \end{tabular} & 65.2 & 262.3 \\ \hline AODV-ACO & \
\begin{tabular}{c} AODV combined with ACO \\ (AODV-ACO) for data \\ called ant-colony optimization with \\ pheromone termite. \\ \end{tabular} & 62.1 & 303.3 \\ \hline ACO-PT &
\begin{tabular}{c} A novel heterogeneous algorithm \\ method based on fuzzy logic. \\ \end{tabular} & 90.2 & 226.3 \\ \hline \hline \end{tabular}
\end{table}