# Enhancing the Performance of XR Environments Using Fog and Cloud Computing
## 4 Experimental Results and Discussion  
This section reports the efficiency of the proposed method by comparing the compression rate and interaction performance when using the edge computer-based compression method in a testbed environment. The experimental setup used a Mini PC (Intel 10 th Gen i5, 16 GB RAM) for the edge computer, while the cloud environment consisted of a self-built personal cloud with 6 th Gen Intel i3 processors and OpenStack [31]. Four touchscreen walls were used, each equipped with two LiDAR sensors, totaling eight sensors, and four projectors were employed. For content delivery, a dedicated server format was used in the fog system, and a rendering-specific client was formed and executed on the output edge. The LiDAR sensor rotated 30 times per second, transmitting 10,000 points per rotation, while the projector had a resolution of 1600 \(\times\) 900, as used in the experiments. The data compression rate experiments indicated an average data compression rate of approximately 70-80%, as listed in Table 1. This compression allowed for the data to be reduced to sizes of 1 KB or less during restoration and transformation into event messages. Hence, in recent network environments, where the amount of data transferred can contribute to network latency in addition to pure response delay, this approach resulted in no network latency between the cloud and the fog because the data size was tiny. As the data compression rate increases, the required computing resources in the fog can be reduced, solidifying the role of edge computers. The second experiment analyzed the interaction latency by monitoring the delay count over five minutes through logs. Table 2 lists the following scenarios: the proposed method was used; only the edge and cloud were used; the edge, fog, and cloud were used; only the cloud was used. When using only the cloud or fog, rendering was replaced with streaming (RTP), allowing for an evaluation of the impact of the rendering method on latency. Unreal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Area** & **User Touches** & **Compression Ratio** \\ \hline Front & 5 & 83 \\ Back & 6 & 81 \\ Left & 5 & 77 \\ Right & 6 & 77 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Average compression rates are based on the number of touches. Engine 4 [32] was used for the real-time streaming approaches [25] without edge computers. In addition, the Unreal Engine was used as the dedicated server [28]. Minimal latency was observed when utilizing edge computers. This result was attributed to the effective compression of most data through edge computing. On the other hand, in scenarios without a fog system, rendering delays and issues related to time synchronization through data updates occurred multiple times, and there were instances of network response latency in the cloud. Therefore, latency issues were minimized when the fog system played a significant role in user interaction, and the edge focused on data compression. The touch recognition speed for the output from the moment of input was measured. This measurement was conducted to provide a clearer comparison of the responsiveness with the existing approaches from previous experiments. Figure 6 illustrates the measurement conducted in the same environment. The delay times were compared based on logs created when rendering in the time-synchronized input/output edge computers. Measurements were taken over time, and the results are based on a one-minute measurement in a 15 fps environment. Figure 6 records the data at 30 frame (two second) intervals for visual clarity and to reduce the amount of visualized data. The proposed method exhibited a very stable pace of user interaction time. With an average delay of less than 100 ms for touch events, users could comfortably engage with the content at 15 fps contents. When an edge computer was involved, it reduced the