# A Compact Low-Power LoRa IoT Sensor Node with Extended Dynamic Range for Channel Measurements
## 4 Measurements and Analysis
### Indoor Signal Level Measurements  
Finally, the indoor setup described in Section 3.2 was considered to gather a third set of signal measurements. These data are presented in Figure 13. They document half a month of indoor LoRa channel measurements performed in the first half of April 2018. The first observation we make on this graph is that the average received powers of the 434 MHz signals (\(\mu_{434,RX1}=-70.9\) dBm and \(\mu_{434,RX2}\) = \(-88.2\) dBm-all averages were calculated using dBm values) are considerably larger than those of the corresponding 868 MHz signals (\(\mu_{868,RX1}=-91.7\) dBm and \(\mu_{868,RX2}=-116.0\) dBm). Hence, it can be concluded that using the 434 MHz ISM-band is indeed a much better option for indoor LoRa communication than using the 868 MHz ISM-band. Considering the signal fluctuations, a clear distinction can be made between data gathered when the building was empty and data gathered when it wasn't. During weekend days, the received signals are much more stable than on week days. Furthermore, these fluctuations mostly disappear by the end of the work day to reappear the next morning. This indicates that the presence of people has a clear and measurable influence on the stability of the LoRa channel. To properly describe this behavior, the mean values and standard deviations of the received powers are calculated for intervals of 8 h during the day (9 a.m.-5 p.m.) and during the night (9 p.m.-5 a.m.). To clarify this, a subset of these  
Figure 12: 10 days of outdoor received power measurements gathered at RX2. Figure 13: 14 days of indoor received power measurements. descriptive statistics can be found in Table 1, describing the second week of indoor measurements at RX1. Now, the averages of the mean values (denoted by \(\bar{\mu}\)) of the subsets of signal data that describe the channel performance when there are people in the building (by day, on working days) and those subsets of data that describe the channel performance when people are not around (at night and in the weekends) are calculated for both bands and both receivers. Moreover, the averages of the standard deviations (denoted by \(\bar{\sigma}\)) of the received powers are calculated for the same subsets (DAY vs. NIGHT, RX1 vs. RX2 & 434 vs. 868). These values can be found in Table 2. Although there is no significant change in mean received signal power, for both receivers RX1 and RX2 and both ISM-bands, there is a clear difference between the standard deviations of the power levels measured when people are around and when they are not. This confirms the claim stated earlier that the channel is less stable when people are present in the building. Signal levels remained well above the detection limit of the LoRa unit configured with the default parameters for the 434 MHz band for both receivers, whereas this was not the case for receiver 2 in the 868 MHz band. As a side note to Figure 13, it should be mentioned that due to the small distance between the transmitter and RX1 and the use of unidirectional LoRa-communication for this test, the detector of RX1 is often saturated when measuring the SNR of packets received in the 434 MHz band. Nevertheless, during week days, this detector comes out of saturation, which further establishes the influence of the presence of people on the quality of the LoRa link. This saturation behavior could be mitigated by signaling this back to the transmitter using bidirectional LoRa-communication. This way, the transmitter could also increase its attenuation settings. Naturally, as was discussed in Section 2.2, this may increase the packet loss of other receivers, illustrating the trade-off between receiving more low-power packets and detecting higher received power values.