## 7 Conclusions  
In conclusion, the TEDDY framework represents a robust and efficient solution for target tracking in edge-based camera networks, achieving substantial reductions in computational load and energy consumption without compromising tracking accuracy. By dynamically activating and deactivating cameras based on the target's predicted trajectory, the framework optimizes resource utilization while maintaining consistent tracking performance. These findings demonstrate the potential of the TEDDY framework as a scalable and adaptable approach, particularly suited for smart cities, emergency response systems, and other resource-constrained applications. The experimental results validate its effectiveness across a range of scenarios, providing a strong foundation for future research and development. However, the TEDDY framework has some limitations that must be addressed in future work. Firstly, the current implementation is focused on single-object tracking, which may not generalize well to multi-object tracking scenarios in high-density environments. Handling multiple targets simultaneously would require additional strategies for camera selection and resource allocation, to maintain efficiency without degrading tracking performance. Secondly, the framework assumes stationary camera placements, meaning that it is not yet optimized for dynamic camera networks such as drone-based tracking systems. Extending the approach to incorporate moving cameras would require novel methodologies for adaptive camera selection and real-time network reconfiguration. Lastly, while the TEDDY framework has demonstrated its effectiveness in reducing computational load, latency has not been extensively evaluated. Analyzing the impact of real-time processing delays and optimizing the trade-off between responsiveness and energy efficiency remain critical areas for improvement. For future research, enhancing the TEDDY framework's camera selection methodology by incorporating geometric information could further improve its precision. Current camera selection methods lack geometric alignment between the projected network matrix and the actual physical camera placements. Integrating geometric data into the selection process would refine the framework's ability to predict target trajectories, enabling more accurate and efficient camera activation strategies. Additionally, extending the framework to address more complex and dynamic environments is crucial. Applications such as high-speed object tracking on highways or multi-object tracking in crowded, fast-paced environments would require advanced tracking algorithms and high-performance hardware tailored to these challenges. In this regard, more sophisticated scheduling strategies or advanced Re-ID algorithms could be explored, to differentiate multiple targets effectively, while further optimizing on-device processing would improve real-time performance in higher-resolution or more crowded scenes. Incorporating adaptive thresholds for IoU and similarity measures may also help the framework better handle rapid target movements or complex lighting conditions. The framework also has potential applications in dynamic camera networks, such as drone-based systems, which require new methodologies for camera selection. Exploring these dynamic networks would enable the TEDDY framework to move beyond static setups and accommodate evolving use cases. Furthermore, for ultra-large-scale camera networks typical of urban environments, efficiency could be enhanced by grouping multiple camera edges into single nodes or employing hierarchical network structures. These modifications would improve scalability while maintaining the framework's efficiency and accuracy, paving the way for its adoption in expansive, real-world applications. Conceptualization, Y.L. ; methodology, J.Y. and J.L. ; software, J.Y., J.L. and I.L. ; investigation, J.Y., J.L. and I.L. ; resources, I.L. ; writing--original draft preparation, J.Y. and J.L. ; writing--review and editing, Y.L. ; visualization, all authors; supervision, Y.L.