## 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
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Author (s)** & **Year** & **Dataset Features and Details** & **Dataset Size** & **Simulation Details** \\ \hline [PERSON] et al. [33] & 2022 & \begin{tabular}{c} Generated using ns-3 simulator. X, Y \\ coordinates, P\({}_{\text{rx}}\) SNR, ACK status. \\ Each ED transmits six uplink \\ packets hourly \\ \end{tabular} & 500 EDs, frequent data collection & NS-3 with a GRU model to dynamically allocate SFs \\ [PERSON] et al. [43] & 2020 & \begin{tabular}{c} Derived from TTN UK formulas. Main \\ power consumption factors \\ from 20 reduced to 15 \\ \end{tabular} & 35,192 entries & Data Loader Techniques. Data scaling (min-max normalization) \\ [PERSON] et al. [31] & 2020 & \begin{tabular}{c} Real LoRaWAN network in Italy. Data \\ from water metering service including \\ SF, SNR, RSSI, etc. \\ \end{tabular} & 372,119,877 packets, \\ [PERSON], et al. [39] & 2024 & \begin{tabular}{c} Generated from network interactions, \\ realistic IoT conditions \\ \end{tabular} & 208 water meters 89,258 EDs & Real-world data analysis \\ [PERSON] et al. [36] & 2020 & \begin{tabular}{c} Generated from network interactions \\ \end{tabular} & \begin{tabular}{c} SF, TP, BW, CR, Max 1000 \\ nodes per slice \\ \end{tabular} & LoRaSim, open-source environment in Python \\ [PERSON] et al. [40] & 2019 & \begin{tabular}{c} Packet rate (0.01 to 2 packets/s), Packet \\ importance (0-1), SNR (\(-23\) to 23 dB), \\ Transmission power (14 dBm), Packet \\ length (15–30 bytes) \\ \end{tabular} & 30 LoRa nodes & 30 LoRa nodes & 30 LoRaSim, open-source environment in Python \\ [PERSON] et al. [44] & 2024 & \begin{tabular}{c} Packet rate (0.01 to 2 packets/s), Packet \\ importance (0-1), SNR (\(-23\) to 23 dB), \\ Transmission power (14 dBm), Packet \\ length (15–30 bytes) \\ \end{tabular} & 2029 records reduced to 1870 records & 30 LoRa nodes & 30 LoRaSim, open-source environment in Python \\ [PERSON] et al. [48] & 2020 & \begin{tabular}{c} Packet rate (0.01 to 2 packets/s), Packet \\ importance (0-1), SNR (\(-23\) to 23 dB), \\ Transmission power (14 dBm), Packet \\ length (15–30 bytes) \\ \end{tabular} & 2029 records reduced to 1870 records & 30 LoRaSim, open-source environment in Python \\ [PERSON] et al. [48] & 2020 & \begin{tabular}{c} Packet rate (0.01 to 2 packets/s), Packet \\ importance (0-1), SNR (\(-23\) to 23 dB), \\ Transmission power (14 dBm), Packet \\ length (15–30 bytes) \\ \end{tabular} & 203 LoRa nodes & 30 LoRaSim, open-source environment in Python \\ [PERSON] et al. [44] & 2024 & \
\begin{tabular}{c} LoRaWAN RSSI measurements \\ \end{tabular} & 2029 records reduced to 1870 records & 30 LoRaSim, open-source environment in Python \\ [PERSON] et al. [48] & 2020 &
\begin{tabular}{c} Number of Messages \\ 130,430 collected in \\ Antwerp, Belgium \\ \end{tabular} & 30 LoRaSim, open-source environment in Python \\ \hline \hline \end{tabular}
\end{table} 
Table 7: \(Cont.\)  
Figure 5: Types of datasets used in the study. The studies [33; 34] predominantly utilized the NS-3 simulator to create detailed synthetic datasets. It offers a realistic environment for testing network protocols and strategies under controlled but diverse conditions, which makes it more effective to simulate resource management strategies as explained in the studies in the literature. Some authors [35; 38] have conducted simulations based on theoretical models as well as custom Python-based simulators developed specifically for their studies. Authors such as those in the study [37] have used LoRaEnergySim (LES) to simulate collided and weak packets with standard LoRaWAN data for Industrial IoT (IIoT) applications but have not explicitly discussed the size of their dataset.