## 4 Pheromone-Based Alarm Delivery Concept
### Assessment of the Proposed Pheromone-Based Approach  
The first set of simulations is aimed to assess the usefulness and efficiency of the basic pheromone strategy (heuristic-P) with the backbone in the trails to deliver alarms to the UAVs. Different numbers of UAVs and threats are considered. The metric used in the evaluation is related to the cost associated with the alarm delivery, which is calculated in terms of the total number of messages that were sent during each simulation run to deliver the alarms to the UAVs. The results presented are average values based on hundred simulation runs for each average, and the heuristic-P method is compared with both an optimal and a flooding-based solution. The optimal solution is assumed to implement the minimum number of hops from the alarm issuer node to the closest UAV, thus minimizing the cost in terms of the number of messages that are transmitted in the network to deliver an alarm, and then it is called Optimum-C. The simulated model of such an optimal solution uses knowledge about the global state of the simulations, which gives access to the positions of all sensor nodes and UAVs, such as to choose the shortest path from the  
Figure 9: **(a)** Representation of the area that a trail can cover; **(b)** Areas used as test case scenarios for simulations with the representation of how much can be covered by a trail. alarm issuer node to the closest UAV. In practice, the implementation of an equivalent solution would imply a very large communication overhead, as each time the movement of a UAV changes its connections to the ground sensor nodes, a flooding to the whole network would be needed to update the new routes in all the sensor nodes. The flooding-based solution considers that an alarm is forwarded by sensor nodes in all directions from the position of the alarm issuer node until the alarm reaches a UAV. A square area of 4 km \(\times\) 4 km was considered as scenario for this first set of simulations. The scenario was simulated with the combinations of three numbers of UAVs (1, 2, and 4), and four different numbers of threats (1, 3, 5, and 7) appearing in the area according to a uniform random distribution within a time window of 180 seconds, which is the pheromone trail lifetime established by the parameters presented above. The rationale for this specific arrival model is to analyze the disturbances that this increasing number of threats may induce in the achieved results in relation to the pheromone trail lifetime. Consistently with this goal, the specific numbers of threats were chosen to stress these possible effects according to the tested numbers of UAVs. Table 2 presents the statistics for the number of messages sent per UAV in this scenario. It is possible to observe the large difference in results provided by an ordinary flooding-based solution, the proposed pheromone-based one, and the optimal reference solution. It is also possible to observe that the proposed solution presents a fairly good efficiency if compared to the optimal one. Besides, the proposed solution scales with the increasing number of appearing threats, as it is graphically shown in Figure 10. Results in Figure 10 indicate that the increase in the number of employed UAVs reduces the amount of messages sent by a rate of 20% in average in relation to the closer setup with fewer UAVs, _i.e._, this reduction is seen when the setup with two UAVs is compared to the one with own UAV and when the setup with four UAVs is compared to the one with two UAVs.