## 5 Results and Discussion
### RQ1: What State-of-the-Art Methods Exist for Optimizing LoRaWAN Energy Efficiency?
#### 5.1.4 State-of-the-Art Datasets and Simulators  
The literature section has reviewed several studies that focused on various aspects of LoRaWAN networks and highlighted how different datasets and simulators are utilized to address issues related to resource allocation, network efficiency, and optimization strategies within LoRaWAN deployments. Table 7 shows a summary of the datasets and simulators used in the literature. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Technique Used** & **Model** & **Application** \\ \hline [PERSON] et al. [30] & 2023 & Supervised ML, RL & EXP4, Lasso algorithm & Large scale IoTs \\ [PERSON] et al. [47] & 2019 & ML based Q-learning & Q-learning & Large coverage areas in LoRaWAN \\ [PERSON] et al. [48] & 2020 & Ensemble Learning & KNN-RFR & Outdoor localization accuracy in LPWAN technologies \\ [PERSON] et al. [49] & 2020 & DRL, cognitive radio systems & DRL & Mobility in dense LoRa networks \\ [PERSON] et al. [50] & 2023 & RL, Multi-Armed Bandit & LP-MAB & (Mobile End Devices) \\ [PERSON] et al. [51] & 2022 & ML & ANN, PSO & Industrial IoT applications. \\ [PERSON] et al. [52] & 2023 & ML & MLR, ANNs & Practical IoT applications \\ [PERSON] et al. [46] & 2020 & Dynamic Transmission & Naive Bayes, & Dense applications in LoRaWAN \\  & & Priority Scheduling & unsupervised clustering & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Summary of ML ensemble techniques. Figure 4: Widely adopted techniques in literature. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author (s)** & **Year** & **Dataset Features and Details** & **Dataset Size** & **Simulation Details** \\ \hline [PERSON] et al. [34] & 2023 & Generated using rs-3 simulator. X, Y & 500 end devices, multiple & NS-3 simulator with long-distance \\  & coordinates, P\({}_{\text{rx}}\), SNR, ACK status. 500 & data points per device & propagation, shadowing, and \\  & EDs, 10 days, packets every 10 min & over 10 days & interference models \\  & & Dense network up to 1000 nodes. & Up to 1000 nodes within a & NS-3, focused on dynamic \\  & & Nodes transmit with SF7. Data & 3 km\({}^{2}\) area & transmission PST using \\  & & included sensor readings. & 3 km\({}^{2}\) area & K-Means clustering \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Summary of datasets and simulators. The studies often used datasets derived from both simulated environments such as NS-3 or OMNeT++ and real-world data collections. For instance, the study by [32] stated the utilization of both model-based data and real-world datasets obtained through Monte Carlo simulations to train ANNs for optimizing EE in LoRa networks. The use of MATLAB for implementing simulations and Microsoft Azure for real-world data training shows the integration of conventional programming environments with cloud-based platforms for robust data processing and simulation. In several studies [32, 36, 37, 39, 51, 52] the general types of data collection parameters were mentioned, such as SNR, RSSI, and packet success rates. It was observed that in most studies [35, 38, 45] the specific attributes of the data, such as exact payload sizes, frequency of data collection per node, or the exact environmental variables measured, were often not mentioned. Also, the study [35] used real-world data for training machine learning models to enhance indoor localization accuracy within LoRaWAN networks. Figure 5 presents the ratio of types of datasets often used in studies.