## 4 Machine Learning and LoRaWAN
### Ensemble Learning, ML, and AI Based Techniques
The authors have proposed a novel algorithm called LP-MAB, which uses the RL and Multi-Armed Bandit (MAB) techniques to configure transmission parameters centrally on the Network Server side. Although LP-MAB was developed based on the ADR algorithm of LoRaWAN, according to the authors, it has offered a more optimal data rate selection strategy for EDs by using the MAB technique. LP-MAB has adopted dynamic network conditions as well as allocated resources effectively and has selected the most optimal data rates for LoRaWAN EDs through exploration and exploitation using RL. The NS-3 simulator was used in the study to evaluate the performance of the LP-MAB algorithm through simulation results. The dataset was created using an NS-3 simulation. The findings of the study showed that the proposed approach has consistently outperformed previous approaches in terms of EC while maintaining a high PDR. It showed improved performance when compared to algorithms such as ADR, BISE, ELS, and SADR. The study also suggested that LP-MAB has good scalability, and it could perform even better with the inclusion of more EDs. The study [51] used ML to enhance LoRa network performance for industrial Internet of Things applications and aimed to improve bit error rate (BER), spectrum efficiency, received power, and outage probability. The study proposed an approach to maximize the received power by using an ANN model for received power prediction and the particle swarm optimization (PSO) technique to identify optimal LoRa configurations. The author used datasets from RSSI and SNR measurements at the LoRa gateway to create simulations by using MATLAB and the LoRaWAN simulator. The results of the simulation demonstrated significant enhancements in BER, spectral efficiency, and outage probability that showed the effectiveness of the proposed optimization approach. The study [51] proposed machine-learning-based Combined Path Loss and Shadowing (CPLS) models to optimize energy consumption in an accurate indoor localization system for the IoT that used LoRaWAN technology. The authors have used two different types of ML models to develop an improved ADR system. They used multiple-linear-regression-based CPLS (MLR) and artificial-neural-network-based CPLS (ANNs). The authors have optimized parameters such as SF, BW, and CR through the proposed ML models. The ML models were trained on an RSSI database that consisted of values that are measured in a commercial office at 19 different locations. The results showed that models have achieved a maximum energy improvement of 43 percent. The study [52] has addressed the critical issue of EE in LoRaWAN networks through clock skew estimation and fine-grained parameter optimization. The authors introduced a novel ML-based multi-agent methodology for clock skew estimation that uses ML models to accurately estimate received RSSI and SNR based on open data sources. The authors have optimized key resources such as SF, CR, and BW. The results showed significant improvements in network size enhancement of up to 21.6%. The proposed clock skew estimation methodology also achieved a network size improvement of up to 66%. The summary of a few authors' work has been discussed in Table 2.