## 4 Machine Learning and LoRaWAN
### Supervised and Unsupervised Learning Techniques
To optimize RSSI measurements in LoRaWAN technology, the researchers used a hybrid methodology that combines statistical models (ARIMA) with ANN and SVM algorithms. The approach involved data preprocessing to extract relevant features such as RSSI values, weather parameters, and time. Significant characteristics that impact RSSI values are found by statistical modeling with ARIMA. The authors have also modeled nonlinear relationships among RSSI, weather variables, and time by using artificial intelligence techniques like ANN and SVM to improve prediction accuracy. The study [25] addressed the impact of collisions on network performance as well as the assignment of SF in LoRaWAN networks. The authors created a simulation environment and proposed a novel, smart SF assignment approach that used Decision Tree Classifier (DTC) and Support Vector Machine (SVM) techniques to evaluate SF assignment schemes. According to the study, simulations using the proposed smart SF assignment approaches produced positive outcomes in terms of packet delivery ratio (PDR). The proposed approach used a simulator to generate many LoRaWAN transmission logs for various topologies. A classifier is then trained using the generated dataset to determine the optimal SF for each network node. The features of the dataset included the X and Y coordinates of the transmission source and the SF of the transmission. The authors were able to enhance network performance by optimizing the SF assignment of nodes. The simulation was performed using the MATLAB simulator on a dataset of messages that were sent across a LoRaWAN network. The LoRa SF allocation was optimized in the study [45], and the author presented a novel K-means clustering-based approach that enabled an adjustable SF allocation approach by taking the range of user distributions into consideration. The study concentrated on a large-scale, unconfirmed-mode class-A LoRaWAN model outage and its probability of optimization without retransmissions. The authors have conducted various simulations based on Stochastic geometry in diverse settings and demonstrated the impact of node distribution on transmission reliability. They evaluated the nodes across SFs and distance from the gateway. They compared the proposed model to baseline models and found that the average coverage probability of the network was improved by up to five points. It also substantially reduced the performance lag between the best and worst-case nodes. The worst-case efficiency was remarkably improved by 1.53 times in some deployment conditions. The study [46] addressed the issue of high packet collision rates in LoRaWAN networks, which is caused by the ALOHA protocol. The authors introduced a novel Dynamic Transmission Priority Scheduling Technique (PST) based on an unsupervised learning clustering algorithm to reduce packet collisions and enhance EE. The proposed dynamic PST approach provided two transmission modes, which are conservative and non-conservative, to enhance the EE of the network. The dynamic PST also applied the unsupervised clustering algorithm as well as the Naive Bayes classifier algorithm to determine the probability of assigning a specific transmission mode to each cluster. Simulation results showed that the proposed methodology has performed better than traditional LoRaWAN applications and improved scalability.