## 6 Discussion  
The TEDDY framework represents a significant advancement in target tracking by leveraging edge-based distributed smart cameras with dynamic camera selection. The results of both experiments validate its effectiveness in optimizing resource utilization without compromising tracking accuracy, highlighting its potential as a practical and scalable solution for real-world applications. In Experiment 1, the framework demonstrated its efficiency in single-person tracking scenarios across various routes and behaviors. The ability to dynamically activate and deactivate edge devices based on the target's predicted path led to an average resource consumption reduction of 20.96%. This substantial improvement underscores the effectiveness of the dynamic camera selection mechanism, which minimizes redundant computations while maintaining consistent tracking performance. The experiment also highlighted the versatility of the framework, as it successfully adapted to diverse behavioral patterns, such as walking, running, and staggering, which pose varying challenges to tracking systems. In Experiment 2, the TEDDY framework was applied to a traffic light adjustment system for emergency vehicles in a simulated urban environment. The results reveal a significant computational load reduction, achieving tracking performance equivalent to the always-on system with only 8-9% of the computational resources. This efficiency was achieved by restricting inference to cameras most likely to capture the target based on trajectory predictions, thereby reducing unnecessary computations. The scalability of the TEDDY framework is evident, as these results suggest that its efficiency would be even more pronounced in larger-scale camera networks with more edge devices. It is important to note that the dynamic activation-deactivation process may cause short delays when a previously inactive camera is brought online. In such cases, a short transition period occurs as the edge device loads necessary models or stabilizes its network connectivity. To mitigate potential gaps in coverage, the TEDDY framework employs a brief buffer interval during which the previously active camera continues to operate until the new camera is confirmed to be online. This overlap effectively prevents missed frames and, since detection and tracking occur locally, the overall impact on real-time performance is minimal. Our experimental results indicate that these delays are negligible, relative to the significant gains in efficiency. Despite these minor delays, the key contribution of the TEDDY framework lies in its ability to balance computational efficiency with operational accuracy, making it well suited for resource-constrained environments. Unlike traditional always-on systems, which are computationally intensive and energy-inefficient, the TEDDY framework provides a scalable and energy-aware alternative. Its success in both single-person tracking and traffic management scenarios demonstrates its adaptability and applicability to a wide range of use cases, from smart city infrastructure to emergency response systems. In conclusion, while the experiments validated the TEDDY framework's efficiency and robustness for single-target tracking, there remain areas that warrant further exploration. Although the framework was initially optimized for lightweight single-target scenarios on resource-constrained edge devices, it can still track multiple targets simultaneously if they reside on different edge devices by performing parallel camera selection and activation via the base station. However, when multiple targets overlap within the same device, the current IoU-based ID assignment--executed prior to feature-based re-identification--may lead to errors that prevent proper Re-ID. Additionally, although advanced multi-camera tracking solutions exist, they typically rely on specialized hardware or proprietary codebases--factors that make them impractical to reproduce on low-power edge devices.