###### Abstract
[PERSON] et al. [27] compared the performance of long-range IoT wireless technologies such as LoRa and Wi-Fi HaLow. Although e LoRa works in higher frequency ranges, Wi-Fi HaLow proved to have higher throughput. Furthermore, the Wi-Fi HaLow network is applicable to situations that current IT infrastructure is incapable of, such as multi-sided applications for medical emergencies [28]. Table 1 shows an overview of the literature review. \begin{table}
\begin{tabular}{c c} \hline \hline
**Studies** & **Focus** \\ \hline [PERSON] [1] and [PERSON] et al. [2] & GPR Theories \\ [PERSON] et al. [4], [PERSON] and [PERSON] & GPR Applications \\ [5], and [PERSON] et al. [6] & GPR Applications \\ [PERSON] et al. [12], [PERSON] et al. [13], and & GPR-AR \\ [PERSON] et al. [14] & [PERSON] et al. [10] & GPR Neural Network \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Literature review summary table. The challenges associated with performing GPR scanning include the following procedures: (1) the operator scanning the entire area, (2) manually mapping and marking the site, (3) presenting the saved data to an expert with spatial coordination, (4) the expert interpreting the data and locating the targets of interest, and (5) the operator returning to the site several times to create full scans of the target infrastructures. By moving the interpretation and processing of GPR data to an edge processing site, the time between collection and onsite interpretation can be greatly reduced. GPR surveys are conducted in diverse environments, ranging from densely populated regions to isolated tracts of land, each providing unique communication challenges. In 2022 [29], the POWDER (Platform for Open Wireless Data-driven Experimental Research) [30] network was evaluated as a potential solution for data transmission to edge processing sites. While the POWDER network showed promise, its implementation area is geographically restricted, leading to the need for more widely available data transmission solutions. This paper proposes a method to overcome these time-consuming challenges by incorporating the Intel RealSense T265 tracking camera for localization, transitioning processing and marking operations to edge-computing sites using the best available network to obtain real-time feedback on processed scans. A method for generating AR representations of the collected F-scans is implemented at the edge, taking advantage of the increased computational power made available by fast network transmission. Using edge computing with a fast network can significantly improve the efficiency of the GPR system. This proposed workflow has the potential to move the processing of raw data out of the office and into the cloud, allowing for faster, onsite interpretation. To accomplish this, both Wireless Local Area Networks (WLAN) and Cellular Networks infrastructures were investigated. A description of the proposed data generation system as well as the arrangement of the WLAN and Cellular transmission modes are provided in Section 2. Results of freeform F-Scan visualization in AR, wireless local transmission, and cellular network data transmission are given in Section 3. Finally, the comparison of the generated results and future directions are provided in Section 4.