## 3 Results and Discussion  
We will demonstrate the functionality of the mathematical apparatus presented in the previous section by applying simulation modelling theory providers. Our focus will be on the stationary distributions \(q_{i}=q_{i}^{0}+q_{i}^{1}\), \(i=\overline{1,S_{w}^{\max}}\), determined using the information technology formulated in Section 2.2, and their first two moments (mathematical expectation and variance). In our calculations, we used the initial values synthesized according to the rules generalized in the form of the corresponding sets. Set 1 is formed on the basis that the facts of receiving SNEIoTD signals \(s^{-}\) (which indicate that a hub received data blocks with errors) are independent and characterized by the probability \(p_{s^{-}}\). At the same time, it was assumed that the probability \(p_{s^{o}}\) of receiving a communication channel overload signal \(s^{o}\) when sending one data block did not change with increases in the value of the parameter \(S_{w}\) for the AI/MD mechanism. Accordingly, in the parametric space \(\{p^{+},p^{+-},p^{o}\}\), Set 1 was characterized by the following analytical expressions: \(p_{n}^{+}=(1-p_{s^{-}})^{n}(1-p_{s^{o}})\), \(p_{n}^{+-}=\left(1-(1-p_{s^{-}})^{k}\right)(1-p_{s^{o}})\), \(p_{n}^{o}=p_{s^{o}}\). Set 2 differed from Set 1 in that, when forming it, we considered the facts of receiving signals \(s^{o}\) from the SNEIoTD environment to be independent. Accordingly, we analytically characterized Set 2 as follows: \(p_{n}^{+}=(1-p_{s^{-}})^{n}(1-p_{s^{o}})^{n}\), \(p_{n}^{+-}=\left(1-(1-p_{s^{-}})^{k}\right)(1-p_{s^{o}})^{n}\), \(p_{n}^{o}=1-(1-p_{s^{o}})^{n}\). The specific ranges of variation in the values of the controlled parameters \(S_{w}\), \(p_{s^{-}}\), and \(p_{s^{o}}\) were chosen to ensure the visibility and interpretability of the obtained results. We determined the visual form of the dependence of \(q_{i}=f(S_{w},\{\text{Set1},\text{Set2}\})\) on \(p_{s^{o}}=10\times 10^{-3}\), \(p_{s^{-}}=\left\{0.5\times 10^{-3},1.5\times 10^{-3},10\times 10^{-3}\right\}\). These calculated results are presented in Figures 3 and 4. Graphs from Figures 3 and 4 demonstrate that if the probability \(p_{s^{o}}\) of receiving a signal \(s^{o}\) is low, then this characteristic parameter does not have a noticeable effect on the shape of the stationary distribution \(q_{i}\) for the entire investigated range of values of the parameter \(S_{w}\). The form of the obtained stationary distributions for different values of the probability \(p_{s^{-}}\) corresponds to expectations and indicates that, for the established initial conditions, the information interaction between the SNEIoTD environment and a hub is almost constantly under the control of the AI/MD mechanism. The transition from Set 1 to Set 2 does not change the nature of the distributions \(q_{i}\), absorbing only the amplitude values of the latter. However, we investigated the sensitivity of the \(q_{i}\) distributions to increases in the value of \(p_{s^{o}}\) by increasing the value of this parameter to the level of \(p_{s^{o}}=25\times 10^{-3}\). We then repeated this calculation of dependences for \(p_{s^{o}}=25\times 10^{-3}\), \(p_{s^{-}}=\{0.5\times 10^{-3},1.5\times 10^{-3},10\times 10^{-3}\}\). The obtained results are visualized in Figures 5 and 6. From the findings presented in Figures 5 and 6, our results show that increasing the value of the parameter \(p_{s^{\sigma}}\) by 2.5 times led to a noticeable increase in the frequency of observation of low TCP Window Size values. At certain ratios of the values of parameters \(p_{s^{-}}\), \(p_{s^{\sigma}}\) distributions \(q_{i}\) degenerate into the form of monotonically decreasing functions. The latter means that the information interaction between the SNEIoTD environment and a hub is almost constantly under the control of the SIS mechanism, which is accompanied by the degradation of the bandwidth of the target connection.