# Unmanned Aerial Vehicle Based Wireless Sensor Network for Marine-Coastal Environment Monitoring
## 5 Discussion  
Although the validation trial only tested the data extraction functional scenario, this controlled trial allowed the validation of each of the systems devices as a whole. Given that the search and rescue is the simpler functional scenario, by doing a data extraction the search and rescue was also  
Figure 17: Water damaged sensor node. corroborated, as the first activity in the data extraction is the localization of the slave nodes and afterwards the recollection of their data, including the GPS position. Analyzing the results obtained in the trials, and comparing them to the proposals of [PERSON] et al. [27] and [PERSON] et al. [28], several improvements were achieved. In terms of wireless data transmissions, the use of LoRa communications was proven to reach a notably higher transmission range of 4 km with a data rate of 5.4 kbps, which is 10 times the range achieved by [PERSON] et al. with an increased data rate. This also further exceeds most buoy based wireless implementations, except for satellite communications, which present additional costs. Moreover, in the work submitted by [PERSON] et al. there was radio interference detected with a secondary radio transmitter mounted in the quadcopter, even though this transmitter and the TinyMesh radios operated at different operating frequencies (433 MHz for the latter, 868 MHz for the former). In the validation tests carried out, it was proven that no interference was detected in the UAV's flight communications by the LoRa transmissions, and vice versa. In terms of low power consumption, the RN2483 LoRa transceiver has a power consumption of 28.8 mA in transmission and 14.2 mA in reception, both at 3 V. In contrast with the radio used by [PERSON] et al., which has a lower energy print compared to [PERSON] et al., the LoRa transceiver consumes less energy in both transmission and reception, even though its performance is similar. All of the previously mentioned facts translate to a lower energy consumption per data packet transmission, which in consequence increases the lifespan of a node, with a greater transmission range. Even though the range limits of the LoRa modulation were not tested in this article, as these have already been validated in other studies [30; 31; 32; 33; 54; 55], for scenarios where a greater transmission radius is desired, there are several things that must be taken into consideration for a proper deployment. Since any UAV is restricted to a finite amount of flight time, it is important to consider the bitrate of the LoRa transmissions, the frequency at which the environmental data is captured and the mode of operation of the UAV. By increasing the spreading factor of the LoRa devices the maximum transmission radius is increased, but consequently the bitrate is reduced. Furthermore, if the data capture frequency is high and the buoys have been left for an extended period of time, high amounts of data will be accumulated on each buoy. With these type of configurations it would at least be required to modify the flight plan of the UAV, first identifying the location of the buoys, then returning home and making a data extraction afterwards. An alternative to this would be to set the UAV for search and rescue mode and later extracting the data, either by collecting the buoys from their location, or to try and set a temporary base station near the buoys location with a master node (connected to a laptop) and manually download the data from the sensor nodes. It is worth noting that in a prior work [32], it was proven that by increasing the spreading factor to 256 chips/symbol transmissions could be done to a receiver station at approximately 7 km. Assuming that this same distance is achievable in a marine-coastal environment, with such a spreading factor, a bandwidth of 125 kHz and coding rate of 4/5, the bitrate would only be reduced to 3.1 kbps but effectively almost doubling the transmission radius.