## 2 Materials and Methods
### Crucial Factors Summarization and Experimental Methodology
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Cruciality** & **Levels** \\ \hline \multirow{3}{*}{Payload size} & Payload size directly affects ToA & \multirow{3}{*}{10–50 bytes} \\  & which has an impact in duty & \\  & cycle compliance. & \\ \hline \multirow{3}{*}{Transmission interval} & An important factor which & \multirow{3}{*}{10–72,000 s} \\  & indirectly related with the overall & \\  & performance of the network and & \\  & finally the kind of & \\  & supported applications. & \\ \hline \multirow{3}{*}{Number of retransmissions} & Power consumption and scalability & \multirow{3}{*}{0–8} \\  & can be affected by this & \\  & ED parameter. & \\ \hline \multirow{3}{*}{ACKs} & Referred to the percentage of EDs & \multirow{3}{*}{On/Off} \\  & that ask for ACKs and it is a QoS & \\  & oriented factor that can affect the & \\  & whole network if not used wisely. & \\ \hline \multirow{3}{*}{TX} & Transmitting power can help & \multirow{3}{*}{0–14 dBm} \\  & achieve greater range but power & \\ \cline{1-1}  & consumption remains an important & \\ \cline{1-1}  & factor for & \\ \cline{1-1}  & LoRaWAN adoption. & \\ \hline \multirow{3}{*}{DR} & The key feature of & \multirow{3}{*}{DR0–DR5} \\ \cline{1-1}  & LoRaWAN that is mainly controlled & \\ \cline{1-1}  & by ADR and affects the overall & \\ \cline{1-1}  & network performance. & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._not consider the impact of collisions resulting from the increasing number of nodes within the same network [29] to be able to manually control parameters independently. Within our study, important insights are expected regarding the impact of the data rate allocation of the EDs. Thus, we need to manually control parameters such as this independently in order to make causality assessments, especially for spreading factors. EDs are class A, while in the vast majority of cases we consider that all EDs are creating messages of common size and transmission interval. For the implementation we developed a simulation program for the LoRaWAN module with the ability to cluster EDs that differ, in their distance from the GW, in transmission parameters (ACK, retransmissions, TX, DR) and in the size of the transmitted package and the interval between successive transmission needs. In addition, some bash scripts were developed for the dynamic modification of the relevant parameters and the repetitive execution of simulations. Experimental assessment of downlinks impact have stayed out of the scope of the simulations because there is no such implementation on the module. The only way we have determined some relevant impact is the acknowledgement scenario in which downlinks are also utilized. External interference has also been bypassed throughout the experiments for the same reason. Figure 1: Simulation scenario with disc end node spatial distribution. Figure 2: Simulation scenario with disc periphery end node spatial distribution. We will use packet delivery ratio (PDR) to make our assessment and to argue on the causality between observations on the communication performance of LoRaWAN. Our experiments include four different dependent variables. These variables are expressing different message transmission failure reasons. All of these dependent variables affect the overall network performance, which is measured in PDR, which is calculated from the total received packets divided by the total packets sent. In our simulations the packets that are not received successfully by at least one GW could have been affected by several circumstances. Interference, sensitivity issues, inability to find a free channel on a GW, or the GW is busy while transceiving a message. Interference is the most common failure reason for collisions while scaling a network. Interference is dependent on the SNR of the transmission. Sensitivity failure is directly related to the distance of a transmission and is dependent on the signal RSSI.