## 3 Proposed Model Techniques
### Machine Learning: A General Overview  
Machine learning is an area of research that is important and rich in information and applications in the IoT field; it involves giving computer programs the ability to enhance their performance depending on old experiences. In general, machine learning consists of different stages, such as data processing, training a model on specific data, and finally, testing the model to evaluate the system's performance [42]. To accomplish this approach, different machine learning techniques are reviewed to evaluate the data prediction accuracy by finding relationships and extracting features between various huge amounts of data. When using a regression approach, the random forest algorithm is considered one of the most powerful ensemble learning techniques, and it combines multiple groups of decision trees to make predictions. The random forest has been compared with other regression algorithms, such as support vector regression (SVR), linear regression, k-nearest neighbors (k-NN), the decision tree (DT), and gradient-boosting machines (GBMs) [43; 44]. Although the random forest algorithm suffers from several limitations, such as intensive computational load, difficulties in fine-tuning for certain high-precision tasks, and greater memory consumption, it has proven to be effective for many factors, such as handling high-dimensional datasets with large and nonlinear features, providing interpretability, demonstrating robustness to overfitting, and handling missing values [45; 46]. Table 2 presents a brief review of the most efficient regression techniques, focusing on the weaknesses and strengths of each one and why the random forest algorithm was selected [47]. While machine learning has innovative applications in wireless communication for enhancing network performance parameters, such as efficiency and security requirements, there are still limitations in its ability to address resource management and help in decision making in situations that depend on IoT applications within LoRaWAN. However, machine learning algorithms have a clear ability to enhance the efficiency of LoRaWANs through dynamic resource allocation, network traffic prediction, interference modification, and energy savings, ultimately refining the network's reliability, capacity, and power consumption [48; 49]. The deep extreme learning machine (DELM) presents several distinct benefits over traditional machine learning methods, neural networks, and other deep learning approaches. Unlike classical machine learning, which often requires manual feature extraction and iterative fine-tuning of the parameters, the DELM leverages random weights for the initialization of the hidden layer and initiative weights for the computation of the output. These features not only make the implementation simpler but also decrease the training times impressively. Unlike conventional neural networks, which may suffer from long and extensive training processes as a result of the backpropagation process, DELM is a one-shot learning method that decreases the computational overhead while maintaining competitive accuracy in tasks such as regression and classification. Furthermore, the DELM's ability to study hierarchical representations through multiple levels of layers improves its ability to capture complicated patterns in data, making it suitable for complicated datasets in various domains such as natural language processing, image recognition, and the analysis of sensor data. DELM models suffer from different limitations in terms of random weight initialization, and they may require difficult and careful configuration to achieve consistent performance. In addition, sometimes, their accuracy can be limited by the lack of a backpropagation technique, which is discarded to speed up the training process. They stand