## 5 Performance Evaluation
The experimental results described above show that MTOW can achieve the highest-performance in FSR compared to the other three methods. These results have also been evaluated in [16]. Moreover, the theoretical analysis in [30] also shows that regret, an  
Figure 10: Number of load channels vs. FSR in the IoT network where the number of IoT nodes is 10,000 with \(\lambda=0.8\) and a duty ratio of 0.5. (**a**) Number of load channels vs. FSR when the number of available channels is 15. (**b**) Number of load channels vs. FSR when the number of available channels is 30. (**c**) Number of load channels vs. FSR when the number of available channels is 60.  
indicator of how much loss was made from the appropriate choices, is smaller for the TOW-based algorithm than that for the UCB1-tuned algorithm. From the experimental results of this paper, it is clarified that MTOW-based channel selection algorithm works properly in a dynamic environment with a huge number of IoT nodes where competition caused by other coexistence IoT exists. Implementation and Performance Evaluation of the Multi-Armed-Bandit-Based Channel Selection Methods on Internet of Things Devices  
In this section, the performance of the channel selection-based MAB algorithms simulated in Section 5 is implemented and evaluated using the actual IoT devices. Wi-SUN IoT devices and LoRa are used as the evaluation IoT network and the interference IoT network, respectively. For the Wi-SUN IoT devices, Lazurite 920J, which supports IEEE 802.15.4g/4e standard and can communicate using the 920 MHz band, is used. Table 2 lists the specification of the Lazurite 920J. Figure 11 shows the settings of the IoT devices in the experiment. The heterogeneous IoT network is constructed in a 13 m \(\times\) 7 m square area. A Wi-SUN IoT network consists of 50 Wi-SUN devices (i.e., Lazurite) and a gateway is deployed in this area. Meanwhile, a LoRa network composed of 5 LoRa devices is also deployed in the area. Wi-SUN devices select the access channels based on the MAB algorithms. A Raspberry Pi controls each Wi-SUN device. The MAB algorithms are implemented on the Raspberry Pi to calculate the access channel and control the Wi-SUN device to transmit data using the selected channel. LoRa devices transmit data using channel 34 every 20 min. The transmission interval for the LoRa device is 10 s.  
Table 3 summarizes the experimental settings. \begin{table}
\begin{tabular}{c c} \hline Operating voltage [V] & 1.8–3.3 \\ \hline Operating frequency &
\begin{tabular}{c} 16 MHz (operation) \\ 32.768 kHz (sleep) \\ \end{tabular} \\ \hline Standby current [\(\mu\)A] & 7 \\ \hline Operating current [mA] & 4 (not using radio) \\  & 25 (using radio) \\ \hline RAM & 6 KB \\ \hline ROM & 64 KB \\ \hline \end{tabular}
\end{table} 
Table 2: The specification of the Lazurite 920J. Figure 11: The layout of the IoT devices in the experiment. Figure 12 shows the experimental results in FSR of the MAB-based channel selection methods, i.e., \(\epsilon\)-greedy based, UCB-1 tuned based, TOW-based, and MTOW-based channel selection method. Wi-SUN devices select their access channel among three channels, i.e., CH34, CH37, and CH40, using the MAB-based channel selection methods. Figure 12 shows that the MTOW can achieve the highest FSR. In addition, the fairness index (FI) values for \(\epsilon\)-greedy based, UCB-1 tuned based, TOW-based, and MTOW-based channel selection method are 0.997, 0.995, 0.974, and 0.998, respectively. Hence, The MTOW-based channel selection method is superior to the other MAB-based channel selection methods in FI and FSR under both simulation and implementation. The experimental results also verify the effectiveness of the MTOW algorithm in dynamic environments, which introduces the forgetting parameter to adapt to the dynamic environment.