## 4 Pheromone-Based Alarm Delivery Concept
### Scalability Analysis  
To assess the scalability of the proposed solution, a second set of simulations was performed for each number of UAVs (1, 2, and 4), in which areas with increasing sizes were considered in addition to the one used as scenario for the first set (4 km \(\times\) 4 km), namely: 2 km \(\times\) 2 km, 6 km \(\times\) 6 km, 8 km\(\times\) 8 km, and 10 km \(\times\) 10 km, as illustrated in Figure 9b. The assessment was done for the same number of threats (1, 3, 5, and 7) and following the same arrival process described above. The presented results are averages for the group of 100 simulation runs, as the ones presented before for the first simulation set. The scalability analysis is based on the cost metric. In order to better evaluate the proposed method, the results for this metric were divided according to the specific parts of the overall strategy (trail-search and trail-follow). Figure 11: Results of the cost metric in terms of the average number of messages sent per threat for the simulations with one Unmanned Aerial Vehicle and the four different numbers of threats (1, 3, 5 and 7). Figure 10: Comparison among the usage of different numbers of Unmanned Aerial Vehicles in terms of number of messages sent for the different setups. To perform a better analysis of the experimental results, the cost metric was normalized as the cost per threat instead of the total cost per setup, as presented in the previous simulation set. This normalization was done by dividing the total number of messages sent by the number of threats considered in each setup. This way of presenting the results makes the comparison of the different setups easier. Figures 11-13 present the results achieved for the cost metric per threat for the different numbers of UAVs. The upper part of the bars represents the contribution due to the trail-search mechanism, while the bottom part represents the contribution due to the trail-follow one. The numbers above the bars provide the average value and the coefficient of variance. The following findings can be outlined:  
1. From all charts in these figures it can be concluded that the cost associated with the trail-search mechanism is higher for the larger areas than for the smaller ones. This is due to the fact that the percentage of the area covered by trails with a given dimension is bigger in the smaller areas than it is in the larger ones. Figure 12: Results of the cost metric in terms of the average number of messages sent per threat for the simulations with 2 Unmanned Aerial Vehicles and the four different numbers of threats (1, 3, 5 and 7). Figure 13: Results of the cost metric in terms of the average number of messages sent per threat for the simulations with 4 Unmanned Aerial Vehicles and the four different numbers of threats (1, 3, 5 and 7). 2. For all numbers of threats and areas an increase in the number of UAVs implies a decrease in the cost associated to the trail-search. The reason for this phenomenon is that, with more UAVs, more trails are available in a given area, increasing the percentage of the area that is covered by them and also increasing the probability of an alarm to find a trail. 3. The cost associated with the trail-search mechanism turns out to be dependent of the number of threats. This is in contradiction with the intuitive observation that, as the results are presented per threat, the number of messages consumed by the trail-search mechanism should not increase with the number of threats. Observing each figure individually and comparing the results achieved for each number of threats, an interesting finding is made. Analyzing the trail-search mechanism (upper part of the charts in Figures 11-13), in Figure 11 it is possible to observe a decrease in the cost metric due to this mechanism from setups with one threat to those with three threats, which is maintained while comparing setups with five to those with seven threats.