## 5 Results
### Experiment 2: A Traffic Light Adjustment System for an Emergency Vehicle  
In Experiment 2, the TEDDY framework was evaluated by comparing its computational efficiency to that of a traditional always-on camera system, similar to the analysis conducted in the previous experiment. To facilitate this comparison, the TEDDY framework, comprising 12 edge devices and a base station, was implemented on a single hardware platform. This setup allowed for a direct performance comparison, demonstrating the advantages of the dynamic scheduling mechanism in reducing unnecessary computations while maintaining high operational effectiveness. The results validate the TEDDY framework's ability to optimize computational resources, particularly in scenarios requiring efficient and responsive tracking, such as emergency vehicle management. In this experiment, we aimed to demonstrate the potential applicability of the TEDDY framework in controlling traffic light systems, particularly in scenarios involving emergency vehicles. While the framework was evaluated for its ability to optimize camera usage and computational efficiency, building and integrating a complete traffic light control system was beyond the scope of this study. Therefore, we focused on simulating the target-tracking and trajectory-prediction capabilities of the framework, which are fundamental components for implementing such traffic control applications. This approach highlighted the framework's potential for future extensions to real-world traffic management systems without compromising the primary focus of this work. In each scenario, we measured the number of processed frames and the computational load for both the always-on camera system and our TEDDY framework, concentrating exclusively on computations related to the tracking task. This focused evaluation ensured a direct comparison of the framework's efficiency in handling tracking operations under identical conditions. The results revealed the advantages of the TEDDY framework in minimizing computational overhead while maintaining tracking accuracy, demonstrating its ability to optimize resource utilization compared to the always-on approach. The detailed results are presented in the subsequent sections, to illustrate these performance improvements. Table 3 summarizes the experimental results, demonstrating the significant computational load reduction achieved by the TEDDY framework compared to the always-on system. By limiting inference to the edge devices where the target is present, the TEDDY framework minimized unnecessary computations and optimized resource utilization. Table 2 shows that the always-on approach continuously activated all 12 cameras, to perform inference for tracking. This means that every camera processed every frame throughout the entire scenario. In contrast, the TEDDY framework activated only the cameras most likely to detect the target, significantly reducing unnecessary computations. The Total Frame represents the total number of frames (or time steps of a scenario) from the start to the end of the scenario. In the always-on approach, all 12 cameras performed inference on every Total Frame simultaneously. Conversely, the TEDDY framework dynamically selected cameras based on the target's predicted path, ensuring that only the selected edge devices performed inference. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenario (Route)** & **Total Frames** & **Always-On** & **TEDDY (Per Camera)** & **Computational Load Ratio** \\ \hline
1 (1-4-5-6-3) & 1267 & 15,204 & 1254 (101, 374, 109, 339, 331) & 8.07\% \\ \hline
2 (7-8-10-9-11) & 1706 & 20,472 & 1697 (127, 464, 150, 625, 331) & 8.63\% \\ \hline
3 (8-10-12-3-2) & 1565 & 18,780 & 1554 (105, 124, 305, 615, 405) & 8.31\% \\ \hline
4 (11-12-10-9-7) & 1491 & 17,892 & 1484 (101, 439, 204, 516, 224) & 8.29\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Inference frames and computation: comparison with and without the TEDDY framework.