## 2 Related Works
However, this technique was not developed with well-defined intelligent approaches, uncertain channel models and ML procedures. Among the solutions developed against various problems of WSNs, the accurate detection and prediction of channel events play a crucial part. [PERSON] et al. [15] proposed a method of analyzing the channel characteristics of WSNs using Support Vector Machine (SVM) and Random Forest (RF)-based Classification model (SMC). SVM and RF are the ML approaches used in the work to understand the nature of real-time wireless channel qualities. This work developed an experimental base for analyzing the channel noise rates, jamming problems, data transfer difficulties and other signaling properties. This work contributed for wireless channel quality assessment practices. This mechanism provided a proper data point collection principles to observe the channel irregularities and uncertainties of wireless sensor networks. At the same time, the suggested channels with optimal conditions were identified as suitable for data communication. On the other side, the supervised models need nonlinear data analysis support systems. In addition, the effort of this mechanism did not identify multi-path channel disturbances, underground uncertainties and unique properties of multiple channels. [PERSON] et al. [16] proposed Optimal Energy-Efficient Transmission (OETN) with naked mole-rate principles. The need for channel parameter estimation and the quality of service model are the important features for WUGSN. The provided model in this work integrated naked mole-rat algorithm and cross layer multi-channel assessment policies to improve wireless channel stability. Since the energy-efficient channel stability model was defined properly, this work achieved the reliable data communication. Notably, the mole-rat algorithm was applied with magnetic induction procedures. This work stated that the generic electromagnetic signaling mechanisms were seriously affected by channel uncertainties. In this regard, this work found the magnetic induction technique for the underground sensor network. The influence of the above work found the solution for ensuring better throughput and minimal energy consumption in WUGSN. In contrast, uncertain induction rates were not evaluated for multiple channels of WUGSN. [PERSON] et al. [17] proposed channel-aware multi-path routing principles using Reinforcement Learning model (CRLR) for underwater sensor networks. Compared with previous works, CRLR delivered a complex channel analysis and learning practices towards underwater channel estimations. In this regard, the CRLR model was proposed to improve wireless routing protocol functions and ensure optimal data communication possibilities under single-path and multi-path routing strategies. In addition, this work found Reinforcement Learning model to optimize the channel energy utilization factors. At the same time, this work confirmed real-time rewards for channel events to improve the channel throughput with minimal energy consumption. However, the CRLR was not developed to meet uncertain channel conditions and vulnerable channels. Similarly, other recent research works proposed various wireless communication strategies, ML applications, data distribution principles, and security measures for WSNs [18; 19; 20]. [PERSON] et al. [21] proposed wireless channel observation techniques against signal jamming attacks with the help of collaborative node mechanisms. The work analyzed the possibilities of jamming problems initiated from other malicious nodes to legitimate sensor nodes. Particularly, the cooperative signal detection model was enabled for maintain ing feasible data communication channels for industrial sensor networks. However, this system was limited in terms of heterogeneity features and uncertain network conditions. The existing systems described above found various real-time problems on handling the wireless channels and underground channels.