# Gathering Big Data in Wireless Sensor Networks by Drone +
## 7 Experiments  
We create simulated experiments to evaluate the proposed algorithm's performance for big data gathering in WSN during the mobile collector movement. We have implemented the Incremental and Decremental heuristics proposed by [PERSON] and [PERSON] [PERSON] and [PERSON] (2018). The only change made on these heuristics was the algorithm for solving the TSP. We included the Concorde Solver (2018), a state-of-the-art algorithm to solve the TSP, to reduce the execution time of these heuristic methods. The original heuristics used a brute force algorithm that increased the execution time for larger monitored areas too much. It is important to mention that the metric execution time is not analyzed here. Both the Concorde and the brute force TSP solver provide the same results since they are exact algorithms. However, Concorde was used to reduce the time we executed our simulation. Then, we applied our algorithm on each tour created by every loop iteration of these heuristics (after lines 19 and 23 of the Algorithms 1 and 2, respectively) and saved the best solution. Hence, the graphs presented in the following show four heuristic methods: Incremental and Decremental, representing the original heuristics, and Incremental-Move and Decremental-Move representing the heuristics with the proposed algorithm. We implemented all methods in Java 8-64 bits. The computer used to run the experiments has a processor Intel Core I7 8565U 1.8 GHz and 8 GB of RAM.  
The experiments were performed in the same scenarios and used the same characteristics described in [PERSON] and [PERSON] (2018), where the Incremental and Decremental heuristics were described. We divided these experiments into two phases. In the first phase, we defined the Scenario 1, that is a small square monitored area with 200 m of side and 30 fixed sensor nodes. In the second phase, we consider a larger monitored area and a larger number of sensor nodes to evaluate the heuristics' performance in WSN with data routes longer than in the first scenario. This phase has Scenarios 2, 3, and 4. All of them have a square monitored area with 400 m of side. Scenarios varied the amount of data stored in each sensor node memory from 20 to 120 kbits. Scenario 3 varied the number of sensor nodes from 100 to 250. Finally, Scenario 4 varied the drone speed from 0.5 to 3.0 m/s. Table 2 summarizes all characteristics of the four scenarios.  
We consider a drone with hovering capability as the mobile sink, such as a quadcopter. It is able to fly and hover over any point in the monitored area. The mobile sink has a radio like the sensor nodes, with the same range of the nodes (r = 60 m). The transmission rate of every link the WSN is 20 kbps. In all scenarios, \(P_{grid}\) is composed of _PDPs_ with 84 m of distance between two of them (up, down, left, and right). In this way, every point inside the monitored area is less than 60 m far from a _PDP_. The time to propagate queries is not considered here. The drone moves at a constant speed and collects data when it is hovering and when it is moving. The main metric is \(T_{total}\). Every point plotted on the graphs represents the average of 33 simulations using different WSN topologies, which provides a 95% confidence interval.  
In Sections 7.1 and 7.2, we compare the performance of the Incremental, Decremental, Incremental-Move, and Decremental-Move methods in the small and large monitored areas, respectively. In Section 7.3, we verify if there is a statistical difference among the methods evaluated here.