# Enhancing the Performance of XR Environments Using Fog and Cloud Computing
## 4 Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Area** & **Our Method** & **Use Edge + Cloud** & **Use Fog + Cloud** & **Use Cloud Only** \\ \hline Front & 1 & 7 & 37 & 92 \\ Back & 0 & 4 & 46 & 108 \\ Left & 0 & 12 & 45 & 112 \\ Right & 1 & 8 & 31 & 99 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Number of occurrences of latency that affects the screen output for five minutes. Figure 6: Comparison of the delay time of various approaches. amount of data transmitted to the cloud significantly, resulting in a consistent response time. The method utilizing both edge and cloud exhibited a slight delay in video playback because it used real-time streaming for rendering. For touch detection, however, it received compressed data, maintaining a consistent speed. On the other hand, when edge computing was not used, the data had to be received and processed from multiple LiDAR sensors over the network. As a result, the transmitted data accumulated over time, leading to progressively slower responsiveness. The same phenomenon was observed when using the cloud only, and it slowed down even faster than with the fog due to slower network latency. In this section, we evaluated the performance of the proposed method in a testbed environment. We compared our system with various system architectures and performances in existing systems that utilize the functionality of commercial game engines. Our system processes a substantial amount of point cloud data received from LiDAR sensors located on the six walls. Handling such large-scale data processing is typically challenging in cloud environments. However, we efficiently addressed this issue by employing edge computing, which involves data compression. Our experiments demonstrated that our system achieved an average data compression rate of approximately 70-80%, minimizing network latency between cloud and fog and allowing for the transmission of smaller data sizes. This reduction in data transfer delays significantly improves real-time interaction, enhancing the user experience in XR environments. Furthermore, we conducted a comparative analysis between fog servers and central servers. In our proposal, fog servers share the same logic as central servers but do not retain data. Instead, they fetch data from the central server when needed. This approach minimizes network latency and effectively resolves time synchronization issues among users. As a result, this configuration provides a clear advantage in response time compared to traditional cloud-based systems, optimizing user interactions, and delivering an improved user experience. Additionally, our system is designed to perform GPU-intensive tasks, particularly screen rendering, exclusively on edge computers. This design ensures that graphics-related tasks, which demand GPU capabilities, are offloaded to the edge, while CPU-based operations, which do not require graphics processing, are carried out within the fog and cloud servers. This efficient division of labor reduces the cost of cloud infrastructure setup and maintenance and effectively distributes the load in online XR environments. As a result, our system offers a more immersive and realistic virtual reality experience. The proposed method used edge computing for the efficient local control of sensors and displays and used fog computing for the rapid processing of user interactions with feedback, synchronizing with the cloud later, enabling faster and more realistic interactions.