# A TDMA-Based Access Protocol for Dense Networks with Moving Nodes for IoT Applications
## 4 Simulation Results
It can clearly be seen that the TDMA-PL algorithm which constitutes a direct transfer of [6] presents a considerable rate of lost packets, with this number rising progressively as the number of EDs increases. This is explained by the fact that all EDs try to send their packets only to the initial time slot assigned to them, while suffering from a potential packet loss in case some of them have moved to a higher SF zone. On the contrary, pertaining to the TDMA-2M, the number of packet loss is reduced to zero for all the cases as the algorithm executes a final GPS measurement before the data transmission and amend its SF accordingly. The direct result of this process though is the increased data latency. MOTILO, on the other hand, achieves decreased data latency with minimum packet losses. proving the ability of the algorithm to adapt to the possible movements of the EDs in zones with a higher SF, since it was given the opportunity to use a time slot belonging to a higher SF. As seen from the Figure 6, for denser network formations, MOTILO faces some packet losses as the increased number of nodes results in longer moving periods and this means increased probability for a two or more SF leaps. However, the proposed protocol maintains the mean data latency significantly lower compared to the TDMA-2M algorithm, as verified by the line graphs presented and analyzed above (Figure 5a-c). From the above performance analysis, it is apparent that MOTILO, being an IoT-oriented LoRa protocol can be utilized to orchestrate moving nodes in wide area coverage applications. As such, forest fire detection applications that combine terrestrial and aerial surveillance means like autonomous robots and UAVs respectively, could be benefited from the low number of lost transmissions and the low mean data latency in order to provide near time information to the monitoring headquarters. Another field of possible use would be the vehicular ad hoc networks (VANET) as the speeds and the number of nodes are in accordance to the usable range of the WuR utilized in MOTILO. That way, LoRa transceivers could be used for mobile data offloading. In agriculture and farming, as mobile network coverage may be limited due to the special characteristics of remote farms, MOTILO could be used to track and synchronize self driving tractors and other  
Figure 6: Average packet loss under TDMA-PL, TDMA-2M and MOTILO algorithms. farming machines. Additionally, MOTILO could be adopted in battlefield as a primary or backup communication system to integrate all military machinery such as tanks, helis, soldier units, moving artifleries, and military drones. The number of supported nodes and their actual velocities do not differ from the scenarios that were investigated in this work. Future protocol amendment would handle unlimited number of SF zone migrations, render the future version of MOTILO an ideal candidate for applications with zero-tolerance in lost packets and minimum latency. Another interesting set of simulation scenarios is shown in Figure 7. In this figure, we test the link between the mean data latency and the speed of nodes and the number of direction changes of the moving pattern. As shown in this figure, there is a slight improvement with the increased speed. This is not a direct relation between the speed of the EDs and the operation of the MOTILO but more a result of the simulation testbed. Due to the operation of MOTILO, the nodes with the higher SFs affect the mostly the total mean data latency as the higher the number of them, the more the latency the system will face as the additional slots that will be reserved will be in the most time-demanding SF zones. For example, a node that is in the SF7 zone will insert a possible ToA(8) time-slot delay while a node in SF1 will insert a possible SF(12) time-slot delay. As the ToA(12) is almost 30 times higher than ToA(7) it become apparent that the EDs at the higher SF zones have a predominant effect to the total Mean data latency.