## 4 Evaluation and Benchmarking
### wM-Bus Connectivity Evaluation  
An important element of the architecture proposed in this paper is the ability of the infrastructure to collect the data produced by the smart water metering devices properly. The fog computing-based data hierarchy depicted in Figure 4 relies on the wM-Bus network to deliver the continuous water quality and usage measurements, collected from the smart devices that constitute the Sensors and Actuators Layer (SAL), to the Mox nodes. Recall from Section 3.2 that the latter are used to form the first edge-based processing sub-layer (EPL-A). In the first set of experiments, we wished to measure the performance of the wM-Bus network in terms of signal quality, the stability of the wireless communication medium and the observed delivery rate. Both the smart metering devices and the Mox nodes were fixed in specific locations, but the quality of the communication was severely influenced by external parameters and installation characteristics. In all cases, the water meters were located inside cast iron-covered inspection pits below their respective buildings, while the Mox nodes were placed in the buildings' upper floors to increase coverage as much as possible. The main hindrance for the communication was the enclosure of the device, which severely diminished the signal's quality, but as we will showcase below, they were also affected by other unpredictable events and environmental agents (e.g., birds). Under normal conditions and without any external interventions, the RSSI of the received messages from three smart meter devices is presented in Figure 5 (Location 1). In this figure, we see the RSSI of the messages received by a single Mox node from three separate smart meters positioned in a single inspection pit of a building. The RSSI value was relatively stable throughout the four months of the evaluation period. Figure 6, on the other hand, shows us an example of an unexpected outside intervention (Location 2). We again see the RSSI of two different smart meters as they were received, again, from a single Mox node. The two smart meters were also placed in the same inspection pit, but in different orientations, thus causing a difference of at least 10 dbs. The sudden drop that happened almost half the time into the evaluation period was caused by a shift in the Mox node's antenna, caused by extreme wind or birds interacting with the antennas of the devices. We can also observe how the first device had more fluctuations in the RSSI values, which became even more intense as the quality of the received signal degraded. The next two figures (Figures 7 and 8) show two cases that were also interesting to note. In Figure 7, we again present the RSSI values from two smart meters recorded from their respective Mox node (Location 3). The second device had a significantly less stable behaviour at the beginning of the evaluation, which was observed also in the first device towards the end of the period, while the RSSI values of the other device stabilized. This gave us the impression that an external agent again affected the quality of the communication, and for some reason, the effect of the agent shifted from one device gradually to the other. Finally, Figure 8 shows a similar case where in the second half of the evaluation period, the RSSI of the first device became unstable and degraded greatly, while the second one was affected at some points, but in a less intense manner (Location 4). Figure 5: wM-Bus smart meter devices with stable RSSI (Location 1). Figure 6: wM-Bus smart meter devices with sudden RSSI drop (Location 2). Figure 7: wM-Bus smart meter devices with unstable RSSI 1 (Location 3). Table 1 presents the absolute numbers for the received and expected packets from all the smart meters in the four locations presented above. We can see that the received packet rate was quite low in some cases, especially when the RSSI dropped closer to \(-100\) dbs.