Table 3 UAV location deployment algorithm based on k-means++

Input: Number of UAV and user location coordinates.

Output: Horizontal position coordinates of UAV.

1. Given the k value of UAV to be deployed.

2. Initializes the location coordinates of the user to be covered.

3. The location of a user was randomly selected as the location of the first cluster center.

4. Go through all users, and get the horizontal distance from all the users to current cluster centers, then select the shortest distance as L_i.

5. The probability of this user that can be selected as the cluster center is calculated as: P_i = L_i²/ΣL_i².

6. The new cluster center is determined according to the probability P_i that the user is selected.

7. Perform steps from 4 to 6 until the k initial cluster centers are determined.

8. The distance between each user and the other cluster centers is calculated.

9. Select the cluster closest to the user and add the user to the list of the cluster.

10. All clusters were traversed and the average of the user coordinates within the cluster list was calculated.

11. The mean values of coordinate were taken as the new cluster center coordinates.

12. Return to step 8 until the cluster center coordinates unchanged or the number of iterations is reached.

13. End.