# The Microverse: A Task-Oriented Edge-Scale Metaverese
## 4 Case Study: A Smart Public Safety Surveillance Microverse for Smart Communities
### Experimental Results  
To validate the feasibility of our proposed microverse SPSS (MSPSS) framework, a prototype was developed using Unreal Engine, accurately modeling an actual engineering building from our testbed premises. The MSPSS prototype included virtual representations of several drones, as shown in the red circles in Figure 5, which reflected their real-world conditions in real time. The prototype achieved swift object detection within this virtual  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Device** & DJI mini 3 & Pixel 4 & Desktop & Quest Meta 3 \\ \hline
**CPU** &
\begin{tabular}{l} Ambarella H6M Image \\ age SOC \\ \end{tabular} & Octa-core & i5-13600K, 5.1 GHz & Snapdragon XR2 Gen 2 \\ \hline
**GPU** & N/A & Adreno 640 & RTX 3090 ti & N/A \\ \hline
**Storage** & 64 GB (SD) & 128 GB & 2 TB & 512 GB \\ \hline
**OS** & Designed App & Android 6.0 & Win10 & Android 12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Configuration of SPSS microverse prototype.  
Figure 4: Screen shots. (**a**) Designed Android app. (**b**) Real-time object detection output.  
Figure 3: SPSS microverse prototype workflow.  
setting by leveraging efficient deep learning (DL) algorithms. In addition, employing Meta software allowed for an immersive, first-person view from the drone cameras, as demonstrated in Figure 6.  
Figure 7 presents the experimental results regarding the delay and throughput, the essential performance factors. Essential benchmarking tests were conducted, focusing on RTSP-based transmission throughput and the end-to-end latency for live streams in UE virtual spaces and VR headsets. Using a bespoke Android application, the video stream was configured at 30 frames per second with a bit rate of 3 Mbps, aiming to reduce latency while maintaining a fluent streaming experience. By calculating the difference between timestamps, the end-to-end delays were evaluated, which refers to the latency between the drone-carrying camera and the live streaming the user experiences in the surveillance room from either the live cast in UE5 or VR headsets.  
Figure 5: Screenshots of UE5-based microverse prototype. (**a**) Both drones are in live-stream mode. (**b**) The first drone switches to detection mode.  
Figure 6: Screenshots of immersive VR experience.  
Figure 7: (**a**) Delay in different resolutions at 30 Fps. (**b**) Streaming throughput in different resolutions at different bit rate settings.  
The tests executed five streaming sessions, each lasting five minutes, at various resolutions. The streaming was considered stable after five minutes for each session, and the end-to-end latency was recorded. A reasonable increase was observed in latency in all categories when the resolution was higher. Notably, the Meta software introduced additional processing time after receiving the stream on the PC, which escalated delays in VR device usage. A potential solution could be developing a specialized streaming app for Meta Quest 3, though it remains a task for the future due to current time limitations. Considering the drone's versatility in supporting different streaming bit rates, tests were conducted at 3 Mbps and 5 Mbps.  
Therefore, the user would not experience high latency (less than one second) either when watching a live feed of cameras in the UE platform and VR goggles or when inspecting the results of an AI-enabled object-tracking procedure. The results validate the feasibility of the microverse system, as the low latency meets the requirements for most scenarios in real-world SPSS applications.