## 5 Results and Discussion
### RQ3: What Are the Parameters That Are Optimized to Enhance the Performance of LoRaWAN Networks?
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author (s)** & **Parameters Optimized** & **Optimization Focus** & **Method of Optimization** \\ \hline [PERSON] et al. [42] & Number of Gateways, Transmission Parameters, SF, Energy Consumption & Strategic planning of gateway deployment and transmission settings based on real geographic data to optimize coverage and reduce energy usage. & ML \\ [PERSON] et al. [32] & TP, SF, CR, BW & Reducing energy consumption and improving transmission reliability through adjusting several parameters. & DL \\ [PERSON] et al. [52] & TP, Path Loss and Shadow Fading, Environmental Variables, ADR & Emphasis on transmission power settings based on environmental conditions. Enhanced network reliability and EE. & ML ensemble Modeling \\ [PERSON] et al. [47] & Resource Allocation, PDR, CSMA/CA, Interference Management & Efficient assignment of frequency channels and improvement in PDR through dynamic adjustments of network parameters to minimize packet collisions and manage interference. & Q-learning \\ [PERSON] et al. [39] & TP, SF & Dynamic adjustments of TP and SF to optimize efficiency in network slices. Focused on EE and collision probability. & FRL \\ [PERSON] et al. [36] & SF, TP, Channel Allocation & Optimization of communication parameters to balance energy consumption, data rate, and reduce interference. & DRL \\ [PERSON] et al. [44] & RSSI & Forecasting RSSI using weather parameters and employing hybrid models to improve prediction accuracy. & ARIMA, AI Techniques \\ [PERSON] et al. [40] & SF, Time on Air, Transmission Duty Cycle (TDC) & Optimizing communication parameters to improve EE and resource usage. & ML, RL \\ [PERSON] et al. [49] & SF, CR, BW & Resource allocation and EE in dense LoRa networks & DRL \\ [PERSON] et al. [45] & SF & Optimizing the allocation of SF to improve network coverage, reliability, and fair resource distribution in large-scale LoRa networks. & K-means clustering \\ [PERSON] et al. [30] & SF, TP, CR & Dynamic optimization of SF, TP, and CR to enhance transmission range, power efficiency, and data transmission robustness. & ML and RL \\ [PERSON] et al. [37] & DER, SF, TP, Physical layer Parameters & Focusing on maximizing DER, balancing SF, and optimizing TP in industrial environments. & RL (SARSA) \\ [PERSON] et al. [35] & SF, TP, DC, Channel Utilization & Tuning of SF, TP, duty cycle, and channel utilization for better network efficiency. & ML \\ [PERSON] et al. [38] & ADR, SF, TP, Channel Utilization & Dynamic adjustment of ADR, SF, and channel utilization to enhance network scalability. & ML \\ [PERSON] et al. [48] & SF, TP, Channel Utilization & Optimizing SP and channel utilization to manage transmission efficiency and reduce collision rates. & ML \\ [PERSON] et al. [41] & TP, SF, ADR & Enhancing TP, SF and ADR for improved EE in underground sensor networks. & Multi-Agent RL \\ [PERSON] et al. [25] & SF, TP, Channel Allocation & Optimizing SF, TP, and channel allocation to reduce time on air and improve EE. & DRL \\ \hline \hline \end{tabular}
\end{table} 
Table 9: _Cont._RQ4: What Gaps and Opportunities Exist for Future Research in the Field of LoRaWAN Performance and Energy Efficiency Optimization? LoRaWAN technology faces several persistent and emerging challenges that impact its efficiency and scalability. Various studies have highlighted critical gaps that offer opportunities for substantial research and development.