# Identifying Misbehaving Greedy Nodes in IoT Networks
## 5 Simulation and Results
### Thresholds Setting  
The identification of the greedy node present in the IoT network represents the ultimate goal of this study. Based on the result of the network parameters analysis of the selfish node summarized in Table 1, we propose the use of a statistical method based on the mean and the standard deviation, as well as an adjustment value \(\alpha\) to determine the \begin{table}
\begin{tabular}{c c} \hline
**Parameters** & **Evaluation** \\ \hline Packets sent & Very high \\ \hline Packets received & Low \\ \hline Collision & High \\ \hline Transmit power & Very high \\ \hline Transmit duty cycle & Very high \\ \hline Power & Very high \\ \hline Radio on (\%) & Very high \\ \hline Radio Tx (\%) & Very high \\ \hline \end{tabular}
\end{table} 
Table 1: Evaluation of greedy node parameters level. Figure 6: Power, APC, and ARDC of greedy and legitimate nodes. minimum and maximum thresholds. For each of the eight thresholds, the \(\alpha\) value must be calculated. For the parameter number of packets sent, the calculating process of the \(\alpha\) value is as follows:  
According to Table 2 representing the network parameter database of N4, the number of packets sent by the greedy node (G) is 20,832 packets, while the upper number of packets sent by a legitimate sender node (S1) is 16,426 packets. Since 16,426 is lower than 20,832, L = 16,426. Hence, according to Equation (3):  
\[\text{L }<\ Th_{max}\ (\text{Packets sent number})\ <\ \text{G} \tag{7}\]  
According to Equation (4):  
\[\frac{20832-5671.190476}{4502.051461}\ <\ \alpha\ <\ \frac{16426-5671.190476}{4 502.051461}\ (2.38\ \leq\ \alpha\ \leq\ 3.37) \tag{8}\]  
The same method of calculating of \(\alpha\) intervals for the threshold \"number of packets sent\" parameter was carried out for each of the remaining 15 simulations. The results are summarized in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Node** & **Packets Sent** & **Collision** & \begin{tabular}{c} **Packets** \\ **Received** \\ \end{tabular}  & \ \begin{tabular}{c} **Transmit** \\ **Power** \\ \end{tabular}  & \begin{tabular}{c} **Transmit** \\ **Duty Cycle** \\ \end{tabular}  & **Power** & **Radio on (\%)** & **Radio Tx (\%)** \\ \hline S 1 & 16,426 & 8439 & 4933 & 0.2 & 0.376 & 1.601 & 1.66 & 0.37 \\ \hline S 2 & 2557 & 1803 & 505 & 0.142 & 0.267 & 1.227 & 1.23  
In order to maximize the efficient detection of greedy nodes, the upper bound of the \(\alpha\) interval should be the lower value among the 16 intervals. Then, the reduction in false alarms is done by maximizing the lower bound. We therefore take the upper value among the 16 intervals provided that this value is lower than the value chosen for the upper limit. Thus, we obtain:  
\[1.5\leq\alpha\leq 1.75 \tag{9}\]  
According to the experimental study carried out on the 16 simulations, the best result is obtained with \(\alpha=1.75\). By replacing the obtained value of \(\alpha\) in (1), the equation of the threshold appropriate to the parameter number of packets sent is:  
\[Th_{max}\ (\text{Packets sent})\ =\ M_{Packets\ sent}+1.75\ \times\ \sigma_{Packets\ sent} \tag{10}\]  
The same method of calculation was applied to determine the eight thresholds appropriate for each of the parameters.