# On the Use of LoRaWAN for Mobile Internet of Things: The Impact of Mobility
## 4 Results and Discussion  
The packet delivery ratio (PDR) was selected as the first metric of interest; it was calculated as the ratio of the total number of messages successfully received by the network server to the total number of messages generated by all LoRaWAN devices during the whole period of the simulation. Figures 3 and 4 show the PDR for different network scenarios (small and large network areas) with different numbers of end devices and mobility speeds using the random waypoint, stationary, and Gauss-Markov mobility models. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline Simulator platform & OMNeT++ \\ Simulation model & INET and FLORA \\ Repetition & 5 \\ Simulation duration & 6 days including 1 day for network warm-up \\  & 1000 m \(\times\) 1000 m (small area) \\ Simulation area & 2000 m \(\times\) 2000 m (large area) \\ Number of nodes & Small area: 500, 1000, 1500, 2000, 2500 \\ Mobility models & Large area: 1000, 2000, 3000, 4000, 5000 \\ Random waypoint wait time & Random waypoint, Gauss–Markov, stationary \\ Mobile end devices & 100\% \\ Path loss model & Log-distance \\ Initial spreading factor & Random between 7 and 12 \\ Transmission power & 14 dBm \\ Mobility speed & 5, 10, 15, 20, 25 m/s \\ Payload packet length & 20, 40, 60, 80, 100 B \\ Time to first/next packet & 1800 s \\ Bandwidth & 125 kHz \\ Coding rate & 4/8 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 3: Packet delivery ratio: (**a**) packet delivery ratio for a network with 20 B payload and 5 m/s speed; (**b**) packet delivery ratio for a network with 2000 \(\times\) 2000 area using the Gauss–Markov mobility model. The best performance was achieved for all scenarios when the number of LoRaWAN devices was at the minimum. This can be justified by the fact that if fewer devices are operating, fewer collisions will occur and, hence, better performance will be achieved compared to networks with a higher number of participating devices. In contrast, the performances of LoRaWAN networks based on different mobility models decrease as the number of devices increases; this is because of the likelihood of traffic interference and collisions. However, LoRaWAN networks based on the random waypoint mobility model maintain better performance compared to networks based on the Gauss-Markov and stationary models for networks consisting of 3000-5000 devices with a speed of 5 m/s, as can be seen in Figure 2(a). The figure also shows that networks based on the random waypoint and Gauss-Markov mobility models maintain better delivery ratios with larger network sizes (4000 and 5000 end devices) compared to the stationary-mobility-based networks. The mobility is seen to be the main reason for this achievement. Devices far away from the gateways may move closer and use a lower spreading factor compared to the static distant devices, which may keep using a larger spreading factor that contributes toward higher possibility of collisions. Regarding the influence of the mobility speed, no effects were experienced for Gauss-Markov-based networks or random-waypoint-based networks, as shown in Figures 2(b) and 2(b), respectively. The total number of collisions is the second metric of interest and is shown for different networks in Figures 5 and 6. Figure 4(a) shows the collisions for a large network for the three mobility models. In general, the collisions show an increasing trend as the number of end devices increases for all the three models. However, in the stationary-based network, the number of collisions is lower than those for the random waypoint- and Gauss Markov-based networks. This is due to the movements of end devices in the other two models, which results in retransmission and a higher probability of collisions. Moreover, mobility causes the NS to modify the SF for those end devices which move farther from gateways to increase their ability to deliver their traffic.