## 3 Case Study and Experimental Results  
A set of experiments have been carried out in a wine production region at the northwest of Madrid, Spain [39] with the purpose of demonstrating the effectiveness of the proposed system. The cultivated parcels where the experiments have been carried out are reported by crop managers to be one of the  
Figure 11: GUI employed in the base-station to aid the crop managers to carry out the frost monitoring mission. Figure 10: Ground-Air system communications. most injured areas from the Spring season frost. It is a major problem in these areas since climate poses a major threat of plant injury due to low-temperature. That is why active frost protection to prevent damage to fruits is required. The protection method is initiated just prior to the air temperature reaching the critical temperature for crop damage and shut down when the low-temperature damage has dissipated. In all cases, frost/freeze protection involves workers monitoring air temperatures throughout the crop. Indeed, all frost/freeze protection systems are costly in terms of capital, labor, and operating costs. Hence, this is the appropriate domain to integrate and test our proposal, by acquiring information of the atmospheric temperature [40], the relative humidity [40] and ambient light [41]. The experiments were limited to three parcels from this critical region. The parcels have approximately an area of 0.5 up to 4 hectares. It should be highlighted that they are separated from each other at a distance of 70 to 100 m.  
To test the reliability of ground communications among the nodes from each mesh network, a preliminary experiment was carried out. In this experiment, 10 motes were used; three in area A, four in area B and three in area C (see Figure 12). This experiment makes it possible to test if nodes are almost randomly distributed (_i.e._, keeping a distance not greater than 30 m between them) in an unstructured terrain and could maintain the network workload. Table 1 shows the overall signal quality and packets lost from each mesh network. This result plays an important role in the workload analysis, since they give a quantitative magnitude of the end-to-end signal accuracy, discarded and resent beacons, and consequently the network lifetime. For a question of simplicity, the parcels have been configured in such a way that each parcel has a single stable communication cluster. Since the WSN from each parcel have their nodes spread with different average dispersions, we can evaluate the distance between nodes that is most favorable, given the site physical characteristics and the number of motes available. The signal quality is obtained for each mote through the follow equation:  
Figure 12: The three parcels adopted for the experimental scenario with the corresponding WSN arrangements. \[\%SignalQuality=\frac{Signal}{Noise}\times SNR\]  
where Signal, Noise, and SNR (Signal-to-noise ratio) are given in db. Moreover, the packets lost for each mode are given by:  
\[\%PacketsLoss=\left(1-\frac{P_{received}}{P_{sent}}\right)\times 100\]  
where \(P_{sent}\) and \(P_{received}\) and stand for packages sent and received, respectively. Therefore the Signal quality and the Packet lost presented in Table 1 are the average value for each parcel. The second set of experiments performed was dedicated to measuring the signal acquired on flight with the dynamic node carried by the quadrotor. The vehicle flew over the aforementioned parcels with the previous average dispersions. In each parcel the dynamic node received beacons from the ground cluster at three different heights, as shown in Table 2. Therefore, the reliability of communication at different heights and the area for the number of nodes to cover (_i.e._, where there is a reliable communication link) by the dynamic node has been analyzed. The results from Tables 1 and 2 illustrate how WSN links are considerably improved with the presence of an elevated node.