# Combined Use of LoRaWAN Medium Access Control Protocols for IoT Applications
## 6 Performance Evaluation
### Assessment in Different Scenarios and Channel Configurations  
Table 8 shows the average PLR of both stationary and mobile end-devices in all scenarios and configurations evaluated. As expected, the PLR values increase with the traffic load, and the highest PLR values are experienced in Case H.  
Furthermore, the results in Table 8 show that in the simulated scenarios, the PLR values obtained by LBT AFA are better than those achieved by Pure ALOHA. In particular, the difference increases with the workload. In fact, when the channels are sensed as busy multiple times, LBT AFA reduces the PLR by leveraging the AFA technique and also the exponential backoff algorithm if all the available channels are sensed as busy, before retrying to transmit. In the SPC scenario, the end-devices that use LBT AFA perform better in Configuration B than in Configuration A, because in Configuration B they use four channels, instead of three. The PLR values obtained in the SPC scenarios are lower than those relevant to the SHC scenario, in which the end-devices that use LBT AFA work on seven channels, because \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline ADR & Disabled \\ Application payload & 20 bytes \\ BW & 125 kHz \\ CR & 4/5 \\ \(LBT_{backoffs}^{max}\) & 5 \\ Mobility pattern & ChiangMobility [81] \\ Propagation Model & LoRa Path Loss Oulu [80] \\ Simulation time & 12 h \\ TP & 14 dBm \\ \(\Delta_{CCA}\) & 160 μs \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Simulation parameters. the SHC scenario does not split the set of available channels. Consequently, the end-devices that adopt the LBT AFA have to compete for the channel access also with the end-devices that use Pure ALOHA. Table 8 shows that no significant differences were found in PLR values between stationary and mobile end-devices. The slightly lower average PLR of the mobile nodes is because most of them transit over an area covered by low SFs. As a result, their transmissions require a low ToA; therefore, their message collision probability is also low because it is less likely that other end-devices will be using the same channel during the transmission of the messages. Table 9 shows the average PDR measured by the end-devices that use LBT AFA. The results in Table 9 show that in the evaluated scenarios, the end-devices that adopt the LBT AFA MAC protocol discard some messages after performing the maximum number of backoffs. In particular, the PDR increases under a high workload, as in this case, the channels are often sensed as busy, and therefore, both the AFA technique and the backoff algorithm are used multiple times. In some cases, a message transmission attempt reaches the maximum number of backoffs without succeeding, and the message is discarded. The PDR values can be reduced by increasing the \(LBT_{backoffs}^{max}\) parameter at the expense of increasing the energy consumption. Finally, the stationary and mobile end-devices obtain similar PDR values. The average and maximum e2 eDelays of the stationary and mobile end-devices obtained in all the scenarios and configurations evaluated are shown in Table 10.  
As shown in Table 10, the Pure ALOHA MAC protocol significantly outperforms LBT AFA in terms of average and maximum e2 eDelay. This is because the end-devices that use