## 2 Related Works
They introduce a bounding technique, reducing energy and time by up to 73%, enabling the derivation of individual node propagation behavior crucial for deriving optimal network-level configurations. This approach, surpassing traditional alternatives like LoRaWAN ADR, allows swift adaptation to environmental changes, resulting in a remarkable 15% performance boost. In another work, [PERSON] et al. optimize the LoRa-based networks by addressing challenges in disseminating global configuration due to regional limitations on ISM bands [29]. They employ tools from the machine learning realm, formulating the updating process as a Reinforcement Learning (RL) problem. This approach results in optimal disseminating policies, enhancing per-node throughput by an impressive 147% compared to established alternatives. [PERSON] et al. proposed a standard-compatible LoRa PHY Software-Defined Radio (SDR) prototype based on GNU Radio [30]. Experiments have been carried out to evaluate LoRa's error rate for coded and uncoded cases to demonstrate that the developed open-source implementation provides a sound basis for further research. They illustrated the end-to-end experimental performance results of a LoRa SDR receiver at low SNR. Table 1 compares the main aspects of various LPWAN and LoRa studies, providing a quick reference to identify their contributions and implications in the field of low-power, wide-area networking. In the landscape of LPWAN research, our work on LoRa technology takes a distinct and impactful direction, addressing a crucial aspect often overlooked--reliability in NLoS conditions. While existing studies have focused on various facets, such as network scalability, propagation models, vehicular communications, and optimization techniques, our research introduces a practical dimension. Real-world experimental measurements consider environmental elements such as plants, buildings, and obstacles, providing a detailed assessment of LoRa's performance. The innovation lies in the introduction of mobility to replicate NLoS circumstances, achieved through an open-source prototype of a LoRa physical layer Software-Defined Radio (SDR), developed using GNU Radio. This approach allows us to capture the dynamic impact of signal diffraction in realistic scenarios. Our comprehensive assessment of reliability, considering BER, SINR, and data rate, contributes to a holistic understanding of LoRa's communication performance under challenging conditions. Beyond theoretical advancements, our work offers practical insights crucial for the real-world deployment of LoRa technology, marking a significant stride in the pursuit of robust LPWAN solutions for IoT applications. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Study** & **Focus** & **Experimental Setup** & **Major Findings** \\ \hline \multirow{2}{*}{[17]} & LPWAN performance in urban & \multirow{2}{*}{Simulation} & Scalability of LPWAN networks with high success rates \\  & scenarios & & \\ \hline \multirow{2}{*}{[18]} & Alternative frequency bands for & \multirow{2}{*}{Not specified} & Path-loss model for extended-range \\  & LPWAN & & LPWAN communication \\ \hline \multirow{2}{*}{[19]} & Wireless tracking system for public & Wireless communication between bus & Cost-effective real-time monitoring \\  & transport & stops and central base station & with minimal power consumption \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Exploring LPWAN landscapes: a comparative overview. Building upon the insights gained from these studies, Section 3 delves into the materials and methods employed in our research, with a specific focus on LoRa technology. We provide a comprehensive overview of LoRa's modulation techniques, equations governing its performance, and the integration of the USRP N321 devices to realize our primary proposed architecture.