## 4 Result and Discussion  
The collection of the locations of infusion pumps monitored using the Sigfox network was completed from 6 to 11 July of 2019. In the 5 days total, excluding installation and retrieval of the target, sensor, and network, the mean average accuracy of the signal received came out at 69.2%. In total, 31 infusion pumps which sent signals to the monitoring system were in 11 hospital rooms, and the actual locations of the infusion pumps were checked physically by working personnel. The correct location signals received out of 31 infusion pumps came out to be 21 (67%), 28 (90%), 23 (74%), 19 (61%), and 17 (54%), in order of each day. The reasons behind signal failures included signal errors due to superposition signals to the same receiver at the same time, power/battery issues of the devices, dashboard error, and the errors in signals coming from infusion pumps at too close a proximity. In December 2019, the in-Hospital Network was used to monitor the signals for locations of infusion pumps as we did with Sigfox. The temperatures of pharmaceutical refrigerators were also monitored during this time. In this case, the number of infusion pumps with signal errors were excluded which increased the accuracy of corresponding data. The average was calculated for each day and the average over the total number of days came out at 72.5% as shown in Table 5. The temperatures collected manually of pharmaceutical refrigerators is recorded as'real temperature' in Table 6 and the data collected through the signals using the hospital network is recorded as 'dashboard'. Figure 11 illustrates the data recorded in Table 6 in a chart format for better visualization. Although the temperature comparison between the real temperature and the dashboard did not correspond 100%, the data was within a reasonable range with a difference within 2 degrees Celsius. With large amounts of data collection happening at the time, the error occurred from superposition signals even with the signal attenuation installed. Some malfunctions of Gateways were also found during the process. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Day \& Time**} & \multicolumn{2}{c}{**Correspondence**} & \multicolumn{2}{c}{**Day \& Time**} & \multicolumn{2}{c}{**Correspondence**} \\ \hline \multirow{4}{*}{Day 1} & \multirow{2}{*}{10:30} & 51.7\% & \multirow{4}{*}{49.4\%} & \multirow{2}{*}{Day 5} & 10:22 & 46.7\% \\  & & (15/29) & & & (7/15) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{14:09} & 41.4\% & \multirow{2}{*}{49.4\%} & \multirow{2}{*}{Day 5} & 14:30 & 88.2\% \\  & & (12/29) & & & (15/17) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{16:10} & 55.2\% & \multirow{2}{*}{} & 16:20 & 94.1\% \\  & & (16/29) & & & (16/17) & \\ \hline \multirow{4}{*}{Day 2} & \multirow{2}{*}{10:10} & 58.6\% & \multirow{4}{*}{76.0\%} & \multirow{2}{*}{Day 6} & 10:10 & 88.2\% \\  & & (17/29) & & & (15/17) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{16:10} & 93.3\% & \multirow{2}{*}{} & 14:25 & 89.4\% \\  & & (14/15) & & & (17/19) & \\ \hline \multirow{4}{*}{Day 3} & \multirow{2}{*}{10:10} & 78.6\% & \multirow{4}{*}{84.3\%} & \multirow{2}{*}{16:37} & 84.2\% \\  & & (11/14) & & & (16/19) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{14:00} & 90.0\% & \multirow{2}{*}{84.3\%} & \multirow{2}{*}{10:43} & 61.1\% \\  & & (9/10) & & & (11/18) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{16:00} & 61.1\% & \multirow{2}{*}{} & 16:54 & 81.0\% \\  & & (11/18) & & & (17/21) & \\ \cline{2-6} \cline{7-7}  & \multirow{2}{*}{16:00} & 61.5\% & \multirow{2}{*}{} & \multirow{2}{*}{AVERAGE} & \multirow{2}{*}{72.5\%} \\ \cline{2-6} \cline{7-7}  & & (8/13) & & & (15/17) & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Location of Infusion Pumps Using Hospital Network. In the most recent data collection in March 2021, the study included temperatures of pharmaceutical refrigerators, locations of moving patients with infusion pumps, and the body temperature of nurses using the non-hospital network with an average accuracy of 83.3%.