## 4 Experiments and Results
\[\text{Channel Uncertainty Rate}=\frac{\sum(\text{n}^{\text{d}}\cdot\text{l}^{ \text{d}})\text{d}\tau}{\text{Nactive}} \tag{26}\]
* \(\text{n}^{\text{d}}\)-Number of node failures in a session;
* \(\text{l}^{\text{d}}\)-Number of link failures in a session;
* \(\text{Nactive}^{\text{T}}\)Number of active nodes in a session. \[\text{Channel Noise Distortion Rate}=\frac{\text{Noise (M)}-\text{Noise (\tau)}}{\text{Nactive}}*100 \tag{27}\]
* \(\text{Noise (M)}^{\text{-Mean Noise Rate}}\);
* \(\text{Noise (\tau)}^{\text{-Noise rate at time }\tau}\). \[\text{Channel Retransmission Rate}=\frac{\text{NL}}{\text{Number of links in a channel}}*100 \tag{28}\]
* \(\text{NL}^{\text{-Total Number of Retransmissions between each link}}\). Equations (24)-(27) describe the network uncertain conditions enabled in the simulation environment. The uncertain channel conditions can be imposed in the WUGSN testbed as channel interference rate, noise distortion rate, retransmission rate, and overall channel uncertainty rate as denoted in the equations. The detailed illustrations of imposed uncertain conditions are provided in Figures 5 and 6. The changes in channel uncertainties are depicted over the changing number of sensor nodes in the network. Interference is the signal generated from other sources. Both types of signals crucially interrupt the channel quality measures [45; 46]. According to the real-time channel distortions, the proposed network model was configured and experimented for evaluating the system performance. As illustrated in Figures 5 and 6, the total number of sensor nodes in WUGSN varies from 20 to 200. The total number of sensor nodes contains both underground sensor nodes and surface nodes. Figure 6 describes the gradual hike of data retransmission rate against uncertain noise production rate in the wireless channels. In this case, the data retransmission rate increases from 5% (0.05) to 45% (0.45) rapidly against the noise rate (0.35 to 0.55). The network channel characteristics experimented in this section show the competent assumptions of the proposed network model [47]. As illustrated in Figure 6, the rapid hike of sensor nodes' data retransmission rate impacts energy consumption rate and network lifetime. Accordingly, the need for efficient channel assessment and learning system is mandatory to activate channel-aware data transmission principles for uncertain WUGSNs. The proposed DMCAP model and the existing models compete to provide reactive data transmission procedures against channel distortions. The models are evaluated using various learning-support performance metrics. In this regard, the proposed and existing techniques are evaluated using system precision, link quality prediction, routing delay, and secure channel throughput against channel uncertainty rate and other factors. Figure 7 clarifies precision rate of different channel assessment techniques. Generally, system precision rate is determined as provided in Equation (29). \[\text{Prec}(\text{System})=\frac{\text{C}\_\text{t}}{\text{T}\_\text{t}}*100 \tag{29}\]  
\(\bullet\)\(\text{C}_{\text{t}}\rightarrow\text{Correctly predicted True channel distortion events}\);  
\(\bullet\)\(\text{T}\_\text{t}\rightarrow\text{Total predicted events as true channel distortions}\). The system precision rate justifies the actual perfectness of the proposed technique (Equation (29)). In this case, the DMCAP outperforms the existing techniques (SMC, CRLR, and OETN). The maximum precision rate of DMCAP is measured as 99.9% where other techniques fall between 94% and 98.5%. Similarly, the minimal system precision rate is recorded as 97.8% for the DMCAP model. At the same time, the existing techniques secure the precision rate from 80% to 91%. In the existing techniques, SMC is identifying optimal channels based on signal phase parameters (in-phase and quadrature phase) through software-enabled radio signaling mechanisms. The samples collected from in-phase and quadrature phase (I and Q) are applied in to SVM and RF functions.