## 4 Machine Learning and LoRaWAN
### Reinforcement Learning (RL) Techniques  
This research study [36] addressed the distribution of network resources, such as SF and TP, in a LoRaWAN. The study aimed to minimize TP with respect to the highest possible data rate. The authors increased network efficiency by using DRL, which created multiple agents that would identify the most optimal transmission parameters for terminal nodes. The authors focused on throughput with respect to energy transmission and compared their proposed approach with the current LoRaWAN's ADR MAX using the NS3 simulator. Findings suggest that the proposed approach has improved throughput by around 15%. DRL agents that were trained to predict optimal values based on network circumstances were also used to optimize SF and TP. This technique aims to reduce transmission power while balancing throughput and reliability. The network may continue to transmit data efficiently while using less energy through the dynamic adaptation of SF and TP. The study [37] aimed to develop an adaptable LoRaWAN strategy that is appropriate for industrial applications by using RL. The authors have proposed a novel method for optimizing LoRaWAN's ADR function to increase the reliability of LoRaWAN networks. The authors have prioritized parameters such as data extraction rate (DER) to improve DER without compromising LoRaWAN standards. Results indicated that around 10% improvement has been observed in DER while evaluated in scenarios with high node density. The study has used SF as an action parameter and used a stochastic discrete approach to achieve optimization of SF and transmission power, which has collectively improved DER and enhanced reliability. The authors conducted extensive simulations using the Python-based simulation tool LoRaEnergySim (LES), which allows the evaluation of LoRaWAN performance under different scenarios. To maximize EE in LoRaWAN, the study [38] proposed a Markov Decision Process model that evaluated node lifespan by considering variables such as transmission energy, channel conditions, and packet size. The authors have adopted RL, specifically Deep Q-Network (DQN), to optimize the durability of nodes. The proposed method was applied to modify factors such as SF, DR, CR, and transmission power level. The model adapted the parameters, which were based on distance from the gateway and expected packet length, to overcome the constraints of transmission range and channel quality. This method significantly increased EE by allowing nodes to operate for extended periods of time without running out of battery power. The LoRa network simulator has used OpenAI Gym as a toolkit, which is widely used for research in RL to replicate the topology and state of the network. The paper [39] presented an optimization strategy to improve network reliability for connected objects on LoRaWAN networks by utilizing federated reinforcement learning (FRL) and network slicing (NS). The model focused on optimizing device throughput by determining the best SF. Multiple agents operate together by using FRL to develop a global policy for network slices, which dedicates resources to each independent slice for throughput optimization. The authors have used MATLAB and Castalia simulators. A neural model with two hidden layers is used for Deep Reinforcement Learning, aiming for convergence to an optimal solution within a feasible training time. In terms of CR, the study maintained it at a constant 4/5 across all network slices, which is chosen for its balanced trade-off between data rate and transmission reliability, and its impact on performance has not been addressed in the study. The authors have used a dataset that included parameters such as collision rate, energy consumption, throughput, delay, and data extraction rate, as well as performance metrics such as packet loss rates and SNR across different network slices. The study noted that a 3% rejection rate for the Low Critical Latency and Efficiency (LCLE) slice has been obtained.