# UAV Path Optimization for Precision Agriculture Wireless Sensor Networks
## 4 Simulation Methodology
operating at speeds between 5 and 40 m/s. As multi rotor UAVs has high-mobility model, we assumed a fast deceleration process during the reading time interval. As the UAV is already inside the SN's communication range, it begins the communication process during this deceleration. In the first scenario, \(S_{1}\), we set the UAV average speed to 25 m/s. We also assume that there is no possibility that two or more SNs are active on the same TS, so we activate the _dedicated timeslots system_. Even so, we activate the UAV learning strategy, ensuring that its displacements between one SN to another are in accordance wth the Prohibition List. Thus, when moving to the next SN, the UAV first checks if the current TS is free. If positive, the UAV flies to the next SN; otherwise, it moves to another SN that is not in the Prohibition List. If there are no possible targets, the UAV waits on its current position until the next timeslot. In the second scenario, \(S_{2}\), we disregard the use of dedicated TSs, that is, they may be shared among SNs. At the moment, we start the simulation, each SN receives a random TS assigned to it and there is a probability that at that moment, one or more SNs start transmitting during the same TS. In addition, we have adopted the strategy of Prohibition List that guide the UAV destinations, and also we set up its average speed to 25 m/s. The purpose of the comparison between scenarios \(S_{1}\) and \(S_{2}\) is to measure the impact caused on the UAV regarding the sharing of TSs between SNs. For the third scenario, \(S_{3}\), we assume a passive SN discovery procedure, where the UAV waits hovering over each SN until it changes to the active state, so it is reconfigured for the TS according to its route for those coordinates. Then, the UAV flies to the next SN in its route and waits again over it, until the SN changes its state to active mode, and so on until all pending SNs configuration is completed. When compared to the first scenario, \(S_{1}\), this scenario aims to identify whether the active mode of SN search is more effective than simply waiting over each SN. For scenario S3, the displacement of the UAV is similar to its original route; therefore, despite the UAV moving less between SNs its average speed is set up to 25 m/s between one SN and another. Furthermore, it is not possible for the UAV to increment and use its Prohibition List due to its waiting time over a single SN while timeslots are passing by; so, it learns information about the states of a single SN. In the fourth scenario, \(S_{4}\), we disable the UAVs learning strategy, maintaining the average UAV speed at 25 m/s. Therefore, we can measure the impact of the Prohibited List and of the decision-making process with respect to the first scenario. A fundamental factor for the whole process is the flight time of the UAV, which directly impacts on its autonomy. Finally, in the fifth scenario, \(S_{5}\), we reduce the SN activation time to a single TS, in order to assess the impact of their reduced availability during the reconfiguration phase. Furthermore, we use dedicated TSs and the UAV average speed is set to 25 m/s. The reduction from the SNs active time has a direct impact on their energy consumption and autonomy; moreover, it makes its search by the UAV harder. Therefore, our goal is to measure the impact over the UAV and SNs with respect to the first scenario. In addition to these five scenarios presented above, we generated results for other scenarios with the UAV average speed set to 15 m/s, with shared and dedicated TS models. However, as the results were very close to those for the above scenarios, we have chosen not to include them next for the sake of brevity. In order to facilitate the comparison between our proposal and the works in [20; 21; 22], we present Table 2 to show the main characteristics of each work. We also explain why it is not feasible to make direct comparisons of our results with theirs in the next Section.