# A Review of Machine Learning-Based Routing Protocols for Wireless Sensor Network Lifetime +
## 5 Machine Learning Adaptability for WSNs
### Supervised Learning  
The supervised learning technique is used to fix several WSN issues, including localization [23], coverage difficulties [24], data collection [25], event discovery [26], routing [27], and target tracking [28]. The following are some supervised learning techniques:  
_Regression:_ As a supervised learning technique, regression makes predictions about a given value (Y) based on a predetermined collection of attributes (X). The regression model uses continuous or quantitative variables. Regression is a relatively basic machine learning technique that produces precise predictions with few errors. _Support vector machines:_ For optimization problems with undefined constraints or complex curves, SVMs provide an alternative solution. When packets cannot pass through the network near the sink nodes, as happens in a WSN, the sink is congested. In machine learning, the \"support vector machine (SVM)\" is a valuable classification process. Localization issues at the border and coverage gaps can be effectively addressed with SVM [29]. _Decision trees:_ \"Decision tree\" refers to a learning algorithm that uses tree-like graphs to practice and evaluate separate functions. Using a decision tree to pick the best node is one of the most important steps in increasing the network lifetime of WSNs [30]. This is accomplished through the CH selection process. In addition, decision trees can be used as a strategy and a technique for localizing important features of linkage reliability, such as loss rate, normal retrieval time, and normal failure time. _Random forest:_ A collection of trees and a classification provided by each tree in the forest make up the random forest (RF) algorithm, which is a supervised machine learning technique. The random forest classifier is created first, and then the outcomes are predicted, which is how the RF algorithm operates [30]. For larger datasets and heterogeneous data, RF performs well. The missing values are correctly predicted using this method. A significant number of decision trees can be produced by isolating variables at each tree node and randomly choosing a subset of training samples. Because of the high quality of training data and overly robust decision trees, the RF classifier's sensitivity level is lower when compared to other streamlined machine learning classifiers. _Artificial Neural Networks:_ As the most popular learning algorithms, NNs are constructed from learning units called decision units. Perceptual and radial basis functions are common names for cascade circuits. Chains of decision units acting in concert can reveal both simple and intricate patterns in data. However, the computationally intensive learning practice requires multiple cascade circuits [31]. _Deep learning:_ Deep learning is a subclass of ANNs and is a supervised machine learning technique used for categorization. The data-learning representation techniques with multi-layer representations (between the input and output layers) are known as deep learning approaches. In order to arrive at the optimal solution, it is composed of basic nonlinear modules that translate the representation from a lower layer to a higher layer. It draws inspiration from the information processing and communication patterns found in human nervous systems [32]. The abilities to extract high-level characteristics from data, work with or without labels, and be trained to accomplish numerous goals are the main advantages of deep learning. _K-nearest neighbor (k-NN):_ \" k-NN is a supervised learning algorithm\" in which the labels of nearby data models are used to classify sample test data. Nearest-average calculations are used to replace missing or unknowable test samples. The _k-NN_ fault detection algorithm relies heavily on this method [31; 32]. _Bayesian learners:_ Without access to the mobility information contained in the WSN sensor node, Bayesian language learners [33] are able to make reliable estimates of sensor node movement (speed).