# Monitoring Perishable Commodities Using Cellular IoT:
## 3 Results and Discussion
### Field Test Results
Luminosity increased as daylight entered through the open doors, marking the end of refrigerated transport. Vibrations ceased entirely once the truck was stationary, indicating the end of transportation-related impacts. For relative humidity, values oscillated between 40% and 80%, with a maximum value of 94% and a minimum value of 34% during the test. CO\({}_{2}\) concentration values ranged from approximately 400 to 2000 ppm, with a maximum peak of about 3800 ppm at the end of the trial. For vibrations, an average value of relative 0 g was observed, both for maximum and average acceleration. The datalogger's battery performance throughout the test also demonstrated reliability, with gradual discharge from an initial 4.2 V when fully charged. Battery voltage declined steadily over the two weeks, with a slight increase in discharge rate during refrigerated phases, approximately between days 51 and 57, where lower temperatures subtly increased power consumption. The battery provided sufficient autonomy for uninterrupted data logging and transmission for the test duration, as anticipated based on prior autonomy results discussed in Section 3.2. Finally, the route map in Figure 16, generated from geolocation data, illustrates the truck's path, correlating with recorded events and confirming the reliability of the system's tracking and data collection in dynamic refrigerated transport conditions. To check the wireless communication performance, the system was prepared to send the connection mode it had established and the signal quality during the test. In this way, it was possible to check if the data were sent through NB-IoT and if, on the contrary, it was able to select other means of communication such as LTE-M, GSM or EGPRS to send the data in case the first one was not available. Table 8 shows the total amount of data measured during the test and by what means they were sent in each case. Besides, it indicates the quality of the signal. As can be seen, during the test, most of the data were sent via LTE-M, which may be due to the fact that the NB-IoT mode takes longer to establish the connection and its timeout may have expired during its connection attempts. Data sent via NB-IoT and LTE-M add up to 73%, which indicates that the vast majority of data was sent with LPWAN networks, optimizing device consumption, which was the initial goal. This may also have depended on the coverage available in the areas where the truck was traveling. In terms of signal quality, 90% of the data were sent with a good signal quality. Based on these results, the microcontroller firmware is being modified to give priority to LTE-M communication. Furthermore, a more comprehensive comparison between the different communication modes (NB-IoT, LTE-M and GSM/EGPRS) in terms of power consumption and efficiency is being considered. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Packet Type** & **Quantity** & **\% Data** \\ \hline Total data measured & 1371 & 100\% \\ \hline Packets sent via NB-IoT & 370 & 27\% \\ \hline Packets sent via LTE-M & 631 & 46\% \\ \hline Packets sent via GSM & 206 & 15\% \\ \hline Packets sent via EGPRS & 164 & 12\% \\ \hline Signal quality \(\geq-52\) dBm & 61 & 4\% \\ \hline Signal quality \(\geq-110\) dBm and \(\leq-53\) dBm & 1179 & 86\% \\ \hline Signal quality \(\geq-114\) dBm and \(\leq-111\) dBm & 131 & 10\% \\ \hline Signal quality \(\leq-115\) dBm & 0 & 0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Data communication results.