## 5 Results and Discussion
### Discussion  
It has been observed while exploring the literature on optimization of LoRaWAN networks that recent research has highlighted the adoption of advanced ML and AI techniques to address key challenges such as EE, resource allocation, and network robustness. The reviewed studies have utilized a range of methods, from DL and RL to ensemble learning, which shows that there are broad and evolving methodologies that aim to enhance the performance of LoRaWAN systems across various operational environments. One of the common areas of focus has been improving EE. Energy efficiency is a critical consideration for networks mainly powered by battery-operated devices. Techniques such as dynamic priority scheduling and ML algorithms like Gradient Boosting and RF have been used to \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (s)** & **Gap Identified** & **Details** & **Future Directions** \\ \hline
[16; 27; 37] & Interference and & High risk of collision & Develop advanced ML algorithms, explore multi-agent systems \\  & Congestion Management & in dense deployments & Refine energy models, develop new technologies for minimal energy use \\
[24; 36; 39] & EE and Optimization & Inefficiency under high-traffic and large-scale deployments & Refine energy models, develop new technologies for minimal energy use \\  & Adaptability to & Inadequate response to & Develop adaptive algorithms, integrate diverse environmental data \\
[16; 28; 30] & Environmental and & environmental changes affecting & Develop simplified algorithms, use predictive analysis for \\
[20; 36] & Operational Variabilities & signal propagation & Enhance clustering algorithms, integrate real-time data for ML models \\
[20; 36] & Computational Overhead & High computational overhead in dynamic environments & Enhance clustering algorithms, integrate real-time data for ML models \\
[17; 39] & Online Resource Allocation & Need for more effective device categorization and behavior prediction & Enhance clustering algorithms, integrate real-time data for ML models \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Gaps and opportunities in literature. intelligently manage network resources, which has reduced power consumption while maintaining and enhancing network performance. For instance, it has been seen that the utilization of DRL has demonstrated effective results in optimizing transmission power and spreading factors in real-time, which are important for adapting to network density and transmission requirements dynamically. Moreover, the capability of these networks to adapt to environmental variables has also been observed as a significant advancement. Those ML models, which specifically used supervised learning, have been proficient at predicting and adapting to changes in network conditions, which is essential for maintaining connectivity and efficiency in diverse settings, from urban centers to underground facilities. This integration of environmental adaptability highlights the potential of AI to bring LoRaWAN into more complex and demanding applications, such as IIoT and smart cities, where diverse and complex network environments exist. The literature also discussed the use of datasets and various network simulators for the evaluation of these models. It has been observed that strategic use of both real-world data and simulated environments has emerged as a crucial approach for advancing LoRaWAN technology. Various authors have used real-world as well as simulated datasets to evaluate the ML and DL models. The adoption of standard simulation platforms such as NS-3, LES, and others allowed researchers to model complex network scenarios and test hypotheses in controlled settings. These simulations are crucial for understanding potential network behaviors under diverse conditions that might not be available or practical to test in a real-world setup due to constraints on resources, time, or environmental control.