## 5 Discussion
### Lessons Learned  
The proposed SAR-IoCA architecture allowed the cooperation of multiple UGVs and a UAV with two teams of first responders to complete a search and rescue mission. Thanks to the integration of information provided from the operation area (UGVs, UAV, H2 WTN, and smartphones), the personnel at the FCC was able to plan and deploy the robots according to the needs of the mission, including the teleoperation of Cuadriga from the FCC.  
The implementation of the X-IoCA architecture showed a resilient performance, highlighted by a failure that happened before the start of the experiments. Our MEC Center located in the operation area (FCC) suffered a power failure, due to the generator powering it running out of gas at one point. By restarting the system, the architecture allowed all functionalities to remain intact. In addition, since the BCC as cloud edge stored all the experiment information in the background, it was possible to save the datasets generated  
Figure 12: Rambler position as a mobile sink next to the area mapped by the UAV.  
until the crash. On the other hand, it was possible to service the rescuers from the BCC while the normal operation of the FCC was recovered.  
In general, a good performance of the H2 WTN was observed, even in the narrowest areas (ravine and tunnel). An important parameter for the LoRa H-WSN is the spreading factor (SF). A lower SF means a shorter time between consecutive packets transmitted from the same sensor-node. This is useful to obtain more frequent information from that sensor-node. However, a lower SF also implies a lower probability of being received by the surrounding concentrator-nodes because their air time is shorter. In addition, the power consumption of LoRa sensor-nodes depends on the SF, with a higher consumption with high SFs. Therefore, we configured a different intermediate SF for the sensor-nodes grouped in a specific SG. The grouping of sensor-nodes in the same location increased the data sending speed, since the different devices transmit the same physical magnitudes acquired, satisfying the time requirements imposed by the use of a frequency band in public use. In the case of the BLE detection network, due to its short range, robots carrying BLE scanners are required to make a slower approach, especially in the case of the UAV as it flies over the areas. In these experiments, data were transmitted in real time from a commercial BLE scanner on board Cuadriga to a database external to the FIS. The other scanners, ESP32-based, onboard Rambler, the UAV, and also Cuadriga, store the data on SD cards. For this reason, the complete analysis of these data was performed offline. The processing of these data allowed us to work on the estimation of the distances between the agent (with the BLE scanner) and the PV (with the BLE transmitter) using models based on the RSSI of the BLE beacons [48].  
The specialist teams involved in the realistic exercises were very satisfied at all levels. From the MEC centers, the fluidity of the audiovisual information facilitated the aerial and terrestrial exploration of the environment, the teleoperation of the UGVs, and the identification of victims. In addition, the physiological sensors were well appreciated by the firefighter who wore them, as he felt comfortable performing his task. However, it would be appropriate for this information to be integrated into the ROS network for monitoring in ROS-FIS or even into a LoRa network [65] for monitoring in SAR-FIS, but this second option would have lower real-time capabilities, given the limitations of LoRaWAN. The monitoring of their physiological signals allows a better evaluation of the risks to which they are exposed, from the MEC centers.