# An Assessment of Environmental RF Noise Due to IoT Deployment
## 10 Conclusions  
This study has developed a simple and cost-effective instrument to collect radio noise environment. The radio noise surveying system (RNSS), built with software-defined radio, demonstrated excellent performance with the equipment noise (EN) being lower than the level captured by the antenna. The results confirm that the RNSS could collect and record ambient radio noise. The system setup and standard operation procedure were quickly repeatable at different candidate sites. While the IoT industry was still not fully deployed, the research innovated a method to generate and simulate radio noise. This paper proposes a strategy to convert big data to a reduced dataset size by removing the zeros and averaging the data by segmentation, resulting in final datasets ready for smooth computing. The exercise handled 165 datasets. The algorithms designed for data analysis demonstrated agility and performance replicable to all datasets featuring the candidate sites, frequency bands, and transmit rates, providing informative results for this study.  
The slopes of the regression lines demonstrate that 80% of the analyzed data developed augmenting trends. Only 20% of the data showed declining trends. Both statistical and empirical analyses concluded with the same observation that the exponential growth of the presence of IoT will generally cause the ambient radio noise to increase irrespective of the candidate sites in urban, suburban, or industrial environments, and irrespective of the operating frequency. Even with low RF power, the IoT applications could still contribute to crowding the radio spectrum and, therefore, could pause risks of limited performance and sustainability when fully deployed at a large scale. This revelation is relevant to science in general, the engineering in the electronic industry, and the regulatory authorities. This study opens up opportunities for future recommendations: (1) recorded data should train machine learning models to predict ambient radio noise levels; (2) the RNSS demonstrated performance repeatability. The same tool should extend the measurement campaign to survey many more candidate sites to provide a broader representative map; (3) this paper limited itself to three frequencies (433 MHz, 868 MHz, and 2.4 GHz). The same routine should be employed to examine radio noise beyond these bands; (4) as this paper allowed random survey times, other research should try to characterize the radio noise environments at specific times, under different weather conditions, and in different seasons.  
Conceptualization, D.G.K.I; methodology, D.G.K.I; validation, N.P. and A.P.; formal analysis, D.G.K.I; investigation, D.G.K.I; data curation, D.G.K.I; writing--original draft preparation, D.G.K.I; writing--review and editing, N.P. and A.P.; visualization, D.G.K.I; supervision, N.P.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding. APC was funded by the Durban University of Technology.  
Not applicable.  
**Data Availability Statement:** Raw data and processed data are available on request.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Band** & **DUT** & **GLM** & **MNC** & **MNS** & **NGM** & **LTRS** & **UTRS** \\ \hline
433 MHz & Tx10 & Tx1 & Tx2 & Tx9 & Tx9 & 2 & 3 \\
868 MHz & Tx10 & Tx10 & Tx10 & Tx10 & Tx6 & 0 & 5 \\
2.4 GHz & Tx8 & Tx9 & Tx10 & Tx10 & Tx4 & 1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Transmit rates of the highest average level of IoT noise.