# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 4 Results and Discussion
However, higher SF results in higher airtime for transmitted packets and hence, increasing the chance of collisions. Figure 4: Packet delivery ratio: (**a**) packet delivery ratio for a network with 1000 \(\times\) 1000 area, 100 B payload, and 25 m/s speed; (**b**) packet delivery ratio for a network with 1000 \(\times\) 1000 area using the random waypoint mobility model. The effect of increasing the device velocity on the number of collisions for a network consisting of 2000 end devices with a packet length of 60 is depicted in Figure 5b. For the Gauss-Markov-based network, the number of collisions remains about the same with a speed of up to 20 m/s. However, for the random waypoint model, the increase in the device velocity has a positive impact on the overall number of collisions, while achieving good results in terms of the PDR, as can be seen in Figure 3a. Moreover, Figure 5a shows that a better performance is achieved with the random waypoint model for the same number of devices within a smaller area. The energy consumption was the third measured metric, and is shown in Figures 7 and 8. The reported value of power consumption is the average consumed energy by each individual end device over the duration of the simulation period; it is calculated as the ratio of the total consumed energy by the whole network to the number of participating end devices in the network. Generally, the average energy consumption by each individual LoRaWAN device increases as the number of participating devices increases for all three mobility models. This is due to interference and possible collisions, which cause the retransmission of lost packets. Thus, higher power is used as the network size grows. Furthermore, the  
Figure 5: Traffic collisions: (**a**) collisions for a network with 2000 \(\times\) 2000 area, 20 B payload, and 5 m/s speed; (**b**) collisions for a network with 2000 \(\times\) 2000 area, 2000 devices, and 60 B payload across different speeds in m/s. Figure 6: Traffic collisions: (**a**) collisions for a network with 1000 \(\times\) 1000 area, 2000 devices, and 60 B payload across different speeds in m/s; (**b**) collisions for a network with 1000 \(\times\) 1000 area using the Gauss–Markov mobility model across different speeds in m/s. average energy consumption is negatively affected by the movement speed. This is due to the changes in devices' locations, resulting in a higher SF in some cases. Consequently, higher packet airtime is required, which consumes more power. As Figures 7a and 8a show, the random-waypoint-based network consumed more energy compared to the Gauss-Markov-based network. This higher consumption is due to the retransmissions that occur in the random-waypoint-based network. The better results reported for the Gauss-Markov-based network owe to the fact that the Gauss-Markov mobility model optimizes historical values of movement when calculating the new values. This technique is used to avoid unexpected changes in the velocity and the direction of an end device. Consequently, the avoidance of unexpected changes in the movement is reflected in the need for fewer retransmissions, which require energy. Regarding the influence of velocity, it should be noted that changes in the movement speed have no considerable impact on the total average energy per device, as depicted in Figures 7a,b and 8b. Figure 8: Energy consumption: (**a**) average energy consumption per end device for a network with 1000 \(\times\) 1000 area, 20 B payload, and 5 m/s speed across different network sizes; (**b**) average energy consumption per end device for a network with 1000 \(\times\) 1000 area using the Gauss–Markov mobility model, with 20 B payload for different speeds in m/s.