## 7 Fundamentals of ML and Taxonomy of Applications
### Supervised Learning  
In this learning method, the function or model is learned using paired, labeled data sets, representing known inputs, outputs, and targets. The website ML uses examples from training data as well as expertise to perform the necessary operations and learn the required behavior. Figure 5 shows the supervised learning process. The ideal example of supervised machine learning is when the parameters for the inputs and outputs in a joint distribution are known and the inputs and outputs can be retrieved from the previous experience of the domain [91]. However, in situations such as body area networks (BANs), where a real distribution or mathematical model is not available, there is no suitable model of channel propagation. In these general learning problems, learning is used to approximate the distribution of the different classes, such as discriminative or generative models, using prior data. The typical applications of supervised learning are classification and regression tasks. Support vector machine (SVM) and k-nearest neighbor (kNN), on the other hand,  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Algo.**} & \multirow{2}{*}{**Ref.**} & \multicolumn{4}{c}{**Solution Technique**} & \multicolumn{4}{c}{**Packet Loss Type**} & \multirow{2}{*}{**Limitation**} \\ \cline{3-3} \cline{5-10}  & & **Traffic Control** & & **Resource Control** & & **6 LoWPAN** & **WSN** & **Buffer Loss** & **Channel Loss** & \\ \hline Back & & & & & & & & & & Simulation not verified \\ pressure & [67] & ✓ & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & & & in real WSN simulators \\  & & & & & & & & & & like cooia \\ GTCCF & [38] & ✓ & \(\times\) & ✓ & \(\times\) & ✓ & \(\times\) & & & High energy \\  & & & & & & & & & & consumption, \\ OHCA & [66] & ✓ & ✓ & ✓ & \(\times\) & ✓ & \(\times\) & & & low throughput \\  & & & & & & & & & & & high energy \\  & & & & & & & & & & & consumption, \\  & & & & & & & & & & low throughput \\  & & & & & & & & & & & \\  & & & & & & & & & & & \\  & & & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Summary of algorithms in congestion mitigation for IoT existing techniques.  
Figure 4: Overview of ML and DL algorithms green boxes for DL algorithms.  
are typical supervised learning algorithms [92]. The authors in [93] proposed a hybrid learning system that helps in dynamic channel tracking and channel estimation. This learning method can be used in downlink communication at the physical layer to eliminate interference and distribute power efficiently. To reduce congestion in the IoT, interference elimination and power distribution are critical. Latency reduction and caching are two of the most commonly used strategies to reduce congestion in wireless WSNs. Intelligent caching, which reduces latency and is used in satellite communications, is one of the most widely used applications of ML [94]. We can use intelligent caching in IoT networks to reduce latency, which reduces congestion in wireless IoT communication networks. Determining the membership of WSNs to the eNB based on content demand is another new case where the monitored ML can be crucial.