## 6 Conclusions  
Industrial IoT applications require wireless communications solutions that not only meet reliability requirements but also demonstrate innovation and technical prowess, especially in harsh environments. In this paper, we examine the performance of LoRa in such high-interference IIoT scenarios, aiming to uncover new insights and demonstrate technical depth. Through careful selection of test parameters and comprehensive analysis of key metrics including SINR, BLER, and data rate, we revealed a deep understanding of LoRa's performance across environments. Our exploration of LoRa technology in high-interference IIoT scenarios not only reaffirms its proven reliability but also sheds light on its theoretical and practical implications, positioning it as an innovative solution. The theoretical consequences of our study extend to the broader landscape of IIoT communications, emphasizing the pivotal role of LoRa in addressing challenges posed by harsh industrial environments. The robustness of LoRa's NLoS conditions distinguishes it from alternative networks, exemplifying its resilience against obstructions and interference. This resilience, coupled with its efficient control over transmission parameters, places LoRa in direct competition with other communication protocols in IIoT applications. The practical consequences of our findings suggest that LoRa stands out as a strong contender in the IIoT realm, offering a technically advanced and cost-effective wireless communications solution. Future research endeavors should explore the integration of LoRa alongside other communication protocols, examining how its unique strengths can complement or surpass those of existing solutions in addressing the specific challenges posed by industrial environments. As future work, the proposed solution will be implemented in different diverse industrial fields. Moreover, a comparative analysis with alternative wireless communication solutions will be performed.  
B.A. contributed by the experiment, measurement, manuscript concept, methodology, original draft writing, visualization, and editing; S.K. and R.C. contributed with the manuscript concept, methodology, original draft writing, editing, and reviewing; C.L. contributed with measurement; K.M. and O.K. contributed by methodology, reviewing, visualization, and editing. All authors have read and agreed to the published version of the manuscript.  
This manuscript was funded by the \"Wandel durch Innovation in der Region (WIR)!--Change through Innovation in the Region\" program implemented by the Smart Rail Connectivity Campus (SRCC) within the research project \"Train Integrity Monitoring System (TIMS)\" (FKZ 03 WIR1206A).  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} ADR & Adaptive Data Rate \\ BER & Bit Error Rate \\ BLER & Block Error Rate \\ BW & Bandwidth \\ CF & Carrier Frequency \\ CR & Coding Rate \\ CSMA & Carrier Sense Multiple Access \\ GNU & GNU's Not Unix! \\ IoT & Internet of Things \\ IIoT & Industrial Internet of Things \\ ISM & Industrial, Scientific and Medical \\ LoRa & Long Range \\ LoS & Line-of-Sight \\ LPWAN & Low-Power Wide-Area Network \\ NB-IoT & Narrowband IoT \\ NLoS & Non-Line-of-sight \\ PHY & Physical Layer \\ RX & Receiver \\ SDR & Software-Defined-Radio \\ SF & Spreading Factor \\ SINR & Signal-to-Noise-Interference-plus-Noise Ratio \\ TP & Transmit Power \\ TX & Transmitter \\ UAV & Unmanned Aerial Vehicle \\ USRP & Universal Software Radio Peripheral \\ V2V & Vehicle-to-Vehicle \\ WSN & Wireless Sensor Network \\ \end{tabular}