## 4 Experimental Settings
### Algorithms
#### 4.1.5 Implementation Details  
In Experiment 1, five Raspberry Pi 5 devices were used as edge devices, each running Ubuntu 24.04 as the operating system (OS) and equipped with 8 GB of RAM. They were chosen for their balance of processing power, energy efficiency, and cost-effectiveness. This setup provided sufficient spatial coverage to track the target's movements while reflecting real-world resource constraints. Although additional edge devices could improve scalability and resolution, our results show that even this limited configuration effectively demonstrates the framework's performance. These devices were responsible for performing local computations as part of the TEDDY framework.  
A computer with an AMD Ryzen 5 5600 HS processor and 16 GB of RAM served as the base station, acting as the central decision-making unit for camera scheduling and target trajectory predictions. The base station also operated on Ubuntu 24.04, to ensure compatibility across the system. The video recordings used in the experiment were captured using cameras with a maximum resolution of 640 \(\times\) 480 pixels, ensuring consistent video quality across all edge devices. To accurately measure power consumption, a Beezap Type-C Voltage Current Tester was utilized. This setup provided a controlled environment, to evaluate the TEDDY framework's performance, in terms of computational efficiency, power consumption, and tracking accuracy while highlighting its suitability  
Figure 8: **(a)** This map represents the simulated urban environment (CARLA) built for conducting the experiment. To predict the direction of the vehicle, cameras were positioned at locations where vehicles might change direction, with arrows indicating the camera locations. **(b)** A scene obtained from Unreal Engine, which was used in the experiment.  
Figure 9: Each scenario route included a total of five cameras. The emergency vehicle started at the location of the first numbered camera in each scenario: **(a)** Scenario 1 followed the route 1-4-5-6-3; **(b)** Scenario 2 followed 7-8-10-9-11; **(c)** Scenario 3 followed 8-10-12-3-2; and **(d)** Scenario 4 followed 11-12-10-9-7.  
for resource-constrained edge hardware configurations. Moreover, to minimize external threats and safeguard sensitive information, the system was deployed on a dedicated, non-internet-facing network. This closed communication environment substantially mitigated common internet-based attacks by ensuring that no direct external route into the network was available.  
In Experiment 2, evaluations were conducted on hardware equipped with a 12 th Gen Intel i7-12800 HX processor and an NVIDIA 3080 Ti GPU with 16 GB VRAM and running Linux 22.04 LTS. Since the evaluation was conducted on a single hardware platform, it was assumed that the camera would be activated 1-to-3 frames later, proportional to the time required for camera selection and communication between the base station and the edge devices. The experiment involved four scenarios with varying frame counts: Scenarios 1, 2, 3, and 4 included 1267 frames, 1706 frames, 1565 frames, and 1491 frames, respectively. These scenarios were designed to test the TEDDY framework's performance in handling dynamic tracking tasks within diverse and realistic settings.