# The Impact of Nonlinear Mobility Models on Straight Line Conflict Detection Algorithm for UAVs
## 6 Discussion and Future Work  
There are two main reasons for missed alarms using SLIDE: the first is when the messages are not received. The second is because a conflict will be considered as detected only if the conflict's position and timing are both correct.  
* Losing messages: This has more effect when using GM and EGM (or nonlinear) mobility, as the drone's direction will change more frequently. However, when using RWP, if a message is received, the drone will stay on the same line for some time, so all (or most) conflicts within the look ahead time can be detected, even if the next message is lost. In other words, there are several chances to detect a conflict, especially with small broadcast cycles. Thus, to avoid losing messages or to minimize its effect,  
Figure 15: Impact of the standard deviation on the number of missed alarms: (**a**) angle SD; (**b**) pitch SD; (**c**) speed SD.  
the broadcast cycle should be small, and the density of the drones must be suitable for the environment.
* Conflict time/place: SLIDE will not only predict if there is a conflict or not, but it also specifies the conflict interval, i.e., the time of the beginning of the conflict and the time of the end of the conflict. Using GM and EGM, the drones will change their heading at each step (the considered update interval is 0.5 s), and the actual position in the near future may differ from the predicted position by SLIDE. As an example, in one of the scenarios, a conflict between two drones happened at the time 20.5; SLIDE using GM predicts that the conflict will start at the time 21.1, the difference here is 0.6 s, and the algorithm considers this as undetected conflict (for the difference in timing). To minimize the errors in calculating the time and place, the changes in the angle and speed should not be large. For the experiments in Section 5.2.1, we tested the algorithm with high values for the standard deviations to present the general performance of the algorithm. The experiments in Section 5.2.3 show that the performance will improve when the standard deviations decrease.  
In summary, to eliminate or minimize the missed alarms, SLIDE parameters, along with the mobility model parameters, need to be adjusted, and the environment needs to be studied carefully. From the experiments, SLIDE is expected to provide acceptable performance in predicting conflicts in large spaces with lower drone speed. Considering all these factors, SLIDE provides acceptable performance in predicting conflicts with nonlinear mobilities, with one missed alarm, on average, in most scenarios for 40 drones when the broadcast cycle is 2 or less. Thus, if we guarantee that all messages arrive and bound the angle change to some upper and lower limits, the errors in calculations will be reduced. This point can be further investigated in the future. Another interesting direction to explore is to study the impact of using 4G communication technology instead of the IEEE 802.11 protocol. From the provided experiments and analysis, it is expected that changing the communication model will directly impact the performance.