## 5 Performance Evaluation
From the simulation results, it can be observed that as the number of nodes increases, FSR decreases. The reason is that with the increase of the IoT devices, the collisions among IoT devices in the same IoT network increase. Even though the FSR decreases with the increase of the number of IoT devices, the FSR is higher than 90% for the TOW/MTOW channel selection method when the number of IoT devices is \(10^{4}\). The reason is that the TOW/MTOW-based channel selection method can select the channel with highest available probability. Hence, the TOW/MTOW is effective for the massive heterogeneous IoT networks. Figure 8 illustrates the relation between the FSR and the duty cycle of the IoT nodes. In this simulation, the numbers of IoT nodes and channels are set to 10,000 and 30. Among the 30 available channels, 12 channels may be loaded by the IoT devices in the other network. \(\lambda\) and the duty ratio are set to 0.8 and 0.5, respectively. The simulation results show that the FSR decreases with the increase of the duty ratio. The reason is that the transmission interval becomes shorter with the increased duty ratio, which will increase the collision probability among IoT devices. Moreover, the MTOW can obtain the highest FSR. The reason is that channel selection-based MTOW can select the channel with the highest available probability. In addition, the MTOW method is more adaptable to dynamic environments due to the introduction of the forgetting parameter, which is introduced to reduce the influence of past experiences. Figure 9 illustrates the relation between the FSR and the number of available channels. In this simulation, the number of IoT nodes is set to 10,000. The number of channels that may be loaded by the IoT devices in the other IoT network is set to 12. The transition parameter \(\lambda\) and the duty ratio are set to 0.8 and 0.5. Simulation results show that the FSR increases with the number of available channels. The reason is that with the increase of the number of available channels, the average number of IoT nodes assigned to each channel is decreased, which reduces the probability of collisions. In addition, the MTOW can get the highest FSR no matter how many available channels there are for the IoT network. The reason is that the introduction of the forgetting parameter can make the method more adaptable to a dynamic environment. Figure 7: Number of nodes vs. FSR in the IoT network, where 30 available channels and 12 channels may be occupied by the IoT devices from the other network with \(\lambda=0.8\) and a duty ratio of 0.5. Figure 10 illustrates the relation between the FSR and the numbers of available and loaded channels. Figure 10a-c show the FSR when the number of available channels are set to 15, 20, and 60, respectively. The number of channels that may be loaded varies from 20% to 80% of the available channels. The numbers of duty ratio and IoT nodes are set to 0.5 and 10,000, respectively. The transition parameter \(\lambda\) is set to 0.8. Simulation results show that the FSR decreases with the increases of the loaded channels. The reason is that collisions among different IoT networks increase when the number of load channels becomes larger. In addition, MTOW can get much better FSR than the other methods when the number of loaded channels increases, which shows that MTOW may be more effective for heterogeneous IoT networks with a larger number of IoT devices. Hence, MTOW could be applied for the heterogeneous IoT network with a higher congestion degree, while UCB1-tuned/MTOW could be used for the heterogeneous IoT network with a lower congestion degree. Figure 8: Duty cycle vs. FSR in the IoT network where there are 10,000 nodes and 30 available channels, while 12 channels may be loaded by the IoT devices of the other kind of network with a communication frequency of \(\lambda=0.8\) and a duty ratio of 0.5. Figure 9: Number of available channels vs. FSR in the IoT network where 10,000 nodes and 12 channels may be loaded.