# EMF Assessment Utilizing Low-Cost Mobile Applications
## 4 Discussion  
In this study, a conversion factor was calculated and used to estimate electric field strength from RSRP values obtained by a smartphone app. The estimated electric field strength values were then compared with actual measurements taken by an exposimeter during a drive test campaign that included multiple repetitions of two different routes. The  
Figure 15: Measured vs. calculated electric field values (v/m) for the second route with data separated manually into (**a**) low-variability segments and (**b**) high-variability segments. accuracy of the estimation was found to be higher in segments where the variability of the received power over repetitions was lower. It is important to note that this variability is not directly related to the accuracy of the electric field estimation from smartphones but instead helps identify areas where phenomena likely to cause poor estimation may occur. Specifically, poor estimation became evident when changes in received power were not consistently captured across all instruments. For example, if the received power of the exposimeter increased by 1.5 times, but the corresponding power from the app increased only by 1.1 times, this discrepancy indicates a problem. These inconsistencies may result either from differences in instrument placement or from changes in the network traffic load that affect only the exposimeter. In the first case, phenomena such as scattering or obstacles causing shadowing can create different LOS conditions across instruments. In the second case, network traffic load variations do not affect the reference signals, which are transmitted at constant power. Both cases lead to high variability in the received power over repetitions, especially in the exposimeter data. Since this method aims to estimate the received electric field strength using solely smartphones, the areas with high variability can be identified manually without the need for data from the exposimeter, by considering the road traffic conditions and the BS height. High road traffic areas combined with a high concentration of SCA or low macro cell antenna masts in relation to the location of the measurement can be identified as high-variability areas. The results presented in the previous section showed that in manually selected low-variability areas, the calculated electric field values were very close to the actual measured values, with \(MAPE\) results close to 30% and \(MAE\) values less than 0.12 V/m. It is worth noting that although in low-variability areas, most phenomena described earlier were less evident, other challenges, such as the exposimeter being partially shadowed by the driver, the smartphone's proximity to the scooter chassis, and the imperfect isotropy of the devices can also cause discrepancies. Additionally, the lack of calibration of the three smartphones, due to the absence of proper equipment, such as a wireless network emulator, suggested that uniformity in received power among the devices cannot be assured, decreasing the accuracy of the estimation. Various studies have utilized mobile apps to estimate exposure. In [29], the Received Signal Strength Indicator (RSSI), which is an indicator that estimates the overall signal strength while considering network traffic load, was used after being adjusted to match the dynamic range of fluctuations in the mobile network, to estimate the actual electric field strength in stationary measurements. While this method takes into consideration the mobile traffic load, it requires the antenna factor of the smartphone or a conversion factor, to convert the RSSI to electric field strength, as well as the network load conditions to adjust the dynamic range of RSSI. Our study addressed the communication load impact by providing a worst-case scenario conversion factor. This approach offered a simple and effective initial estimation of actual electric field strength, which remained accurate even under more challenging measurement conditions, such as during a driving test.