## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**SF Allocation Scheme** & **Approach and References** & **Publication Year** & **Contribution** \\ \hline \multirow{3}{*}{ADR} & \multirow{3}{*}{Adaptive data rate [11]} & \multirow{3}{*}{2019} & The ADR scheme is a default mechanism of LoRaWAN that allows the network to dynamically adjust the data rate and spreading factor (SF) of a device’s transmission based on its signal quality and network load. \\ \hline \multirow{3}{*}{SNR-based SFs} & SNR-based allocation [13] & \multirow{3}{*}{2021} & This paper introduced an SF allocation scheme based on the target Signal-to-Noise Ratio (SNR) of the received packet and the impact of channel fading and distance. \\ \cline{2-4}  & Best-Equal LoRa (BE-LoRa) [14] & \multirow{3}{*}{2022} & This paper introduced an SF allocation scheme through the optimization and balancing of the SINR of received packets at the gateway for proper power setting and SF updates. \\ \hline \multirow{3}{*}{RSSI-based SFs} & \multirow{3}{*}{EXPLoRa-SF [15]} & \multirow{3}{*}{2017} & This paper introduced a SF allocation scheme where SFs were distributed equally to the nodes based on their RSSI values to create coexisting orthogonal sub-channels in the same channel bandwidth, enabling simultaneous communication among all nodes. \\ \hline \multirow{3}{*}{Time-on-Air-based SFs} & \multirow{3}{*}{EXPLoRa-AT [15]} & \multirow{3}{*}{2017} & This paper introduced an SF allocation scheme by balancing the total offered packets (load) through the ordered waterfilling of SFs that then guaranteed the ToA equalization in each SF group. \\ \cline{2-4}  & \multirow{3}{*}{EXPLoRa-C [16]} & \multirow{3}{*}{2021} & This paper introduced an SF allocation scheme by demonstrating a concept of sequential waterfilling, where nodes were firstly ordered based on their RSSI values and then were allocated to the given SF group until the total offered packets in the group is full. \\ \hline \multirow{3}{*}{Distance-based SFs} & \multirow{3}{*}{EIB and EAB [12]} & This paper proposed two SF allocation schemes: EIB, which divides the network area into concentric circles of equal intervals, and EAB, which uses equal areas. In the EIB scheme, lower SFs are assigned to inner annuli, resulting in higher packet success probability in interference-prone environments. Conversely, the EAB scheme improves packet success probability at longer distances by mitigating near–far effects in smaller outer annuli. \\ \hline \multirow{3}{*}{\begin{tabular}{c} Machine learning-based SFs \\ \end{tabular} } & \multirow{3}{*}{2023} & This paper proposed the ER-RL algorithm, which is a reinforcement learning algorithm designed to optimize SF allocation based on channel traffic equilibrium, aiming at mitigating packet collisions. Nodes will receive positive rewards for successful packet delivery and negative rewards otherwise. \\ \cline{2-4}  & LR-RL [17] & This paper proposed the SF allocation scheme by treating the SF allocation as a contextual multi-arm bandit problem, which was solved for packet reception ratio (PRR) maximization using the decentralized EXP4 reinforcement learning algorithm that can converge quickly. \\ \hline \multirow{3}{*}{Descriptive Statistics-based SFs} & \multirow{3}{*}{\begin{tabular}{c} Quantile classification \\ of the variance from \\ the mean [19] \\ \end{tabular} } & \multirow{3}{*}{2021} & This paper introduced an SF allocation scheme where the quantile classification was applied to assign new SFs based on the mean and probability density function (PDF) of the RSSI data in each original SF group. \\ \cline{2-4}  & \multirow{3}{*}{\
\begin{tabular}{c} Standard deviation \\ classification [20] \\ \end{tabular} } & This paper introduced an SF allocation scheme by using the Geometric Distribution (GD) algorithm, a novel inferential statistics approach to address the challenge of non-uniform or biased SF node distribution. \\ \cline{1-1} \cline{3-4}  & \multirow{3}{*}{
\begin{tabular}{c} Geometric Distribution \\ (GD) \\ \end{tabular} } & \multirow{3}{*}{2024} & The algorithm possesses flexibility in finely tuning the weight factor (\(w\)) via an intricately linked geometric distribution factor (\(p\)-value) until the optimal \(p\)-value and corresponding weight factor (\(w\)) that yields the highest DER of the network are identified. \\ \hline \hline \end{tabular}
\end{table}