## 2 Literature Survey
### Related Work
(2023) presented an innovative, reliable, energy-efficient model assisted by beamforming with a firmware update over the air (FUOTA) for LoRa networks; this system was named FLoRa, and it featured several techniques, including delta scripting, channel coding, and beamforming. The FLoRa model enhances the network reliability for transmission by up to \(1.51\times\) and the energy efficiency by up to \(2.65\times\) compared with the present solutions in LoRaWAN. Table 1 provides a summary of the most relevant works discussing oil pipeline monitoring systems, specifically the enhancements of LoRaWAN network performance in these and other systems. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author(s)/Year** & **Technique Used** & **Problem Addressed** &
\begin{tabular}{c} **Network Parameter** \\ **Enhancement** \\ \end{tabular} \\ \hline [PERSON] et al. & Employing the electromagnetic & Determining the efficient & \\
[11]/2015 & approach based on a wireless & communication distance between & No \\  & underground sensor network. & sensing nodes. & \\ \hline [PERSON] et al. & Ant colony optimization (ACO) & Linear sensor placement for oil & No \\
[12]/2015 & and genetic algorithms (GAs). & pipeline monitoring. & \\ \hline [PERSON] et al. (2020)/2020 & \
\begin{tabular}{c} Deep learning (DL) approach \\ with LoRa technology. \\ \end{tabular} &
\begin{tabular}{c} Achieving the optimal \\ transmitting power, which is \\ useful for maximizing energy \\ efficiency in LoRa networks. \\ \end{tabular} & Yes \\ \hline [PERSON] et al. & A lightweight scheduling & \\
[14]/2018 & Algorithm. & Reducing the capture effect. & Yes \\ \hline [PERSON] et al. & Message replication and gateways & Temporal and spatial diversity of \\
[15]/2018 & with multiple receiving antennas. & LoRa networks. & Yes \\ \hline [PERSON] et al. (2020)/2020 & \begin{tabular}{c} LoRaWAN multi-armed bandit \\ and reinforcement learning (RL) \\ algorithm. \\ \end{tabular} & \begin{tabular}{c} Resource allocation solution for \\ LoRa endpoints. \\ \end{tabular} & Yes \\ \hline [PERSON] et al. (2017)/2021 & \
\begin{tabular}{c} Deep reinforcement learning \\ algorithm for multi-channel \\ resource allocation in LoRa \\ networks. \\ \end{tabular} &
\begin{tabular}{c} Enhancing the packet delivery \\ ratio (PDR) and ensuring lower \\ power consumption to increase \\ the lifetime and network capacity. \\ \end{tabular} & Yes \\ \hline [PERSON] et al. & Decreasing the sensing interval & Predicting the amount of energy \\
[18]/2022 & and increasing spreading factor of & consumed for each node. & Yes \\ \hline [PERSON] et al. & \
\begin{tabular}{c} Smart spreading factor (SF) \\ assignment method that \\ depended on artificial neural \\ networks. \\ \end{tabular} &
\begin{tabular}{c} Selecting optimal SFs to reduce \\ collisions and energy \\ consumption. \\ \end{tabular} & Yes \\ \hline [PERSON] et al. & LoRaWAN-pipeline-based & Detecting and localizing leaks in \\
[21]/2023 & flow-rater approach. & pipelines. & No \\ \hline [PERSON] et al. & Deep reinforcement learning and & \
\begin{tabular}{c} Reallocating the spreading factors \\ (SFs) and adjusting the \\ transmission powers (TPs) to \\ achieve network performance \\ enhancement. \\ \end{tabular} & Yes \\ \hline [PERSON] et al. (2023)/2023 &
\begin{tabular}{c} Application of a pressure sensor \\ and GSM model to gather and \\ send pressure information in a \\ real-time manner. \\ \end{tabular} & Monitoring kit for pressure rate & No \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the most relevant work.