**Acknowledgements**

This work was supported by project No D-098-2019 "Monitoring and analysis of the spectrum occupancy and interference in ISM ranges for the implementation of reliable IoT applications."

**235**

**Author details**

Antoni Ivanov\*, Viktor Stoynov, Kliment Angelov, Radostin Stefanov,

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Dimitar Atamyan, Krasimir Tonchev and Vladimir Poulkov

\*Address all correspondence to: astivanov@tu-sofia.bg

Technical University of Sofia, Sofia, Bulgaria

provided the original work is properly cited.

*Interference Mapping in 3D for High-Density Indoor IoT Deployments*

*DOI: http://dx.doi.org/10.5772/intechopen.93581*

*Interference Mapping in 3D for High-Density Indoor IoT Deployments DOI: http://dx.doi.org/10.5772/intechopen.93581*

*Wireless Sensor Networks - Design, Deployment and Applications*

**Figures Value (mm)**

even in the higher levels of elevation.

**4. Conclusion**

**Table 3.**

*Coordinates in x- and y-axes.*

**Acknowledgements**

reliable IoT applications."

When it comes to scenarios **S5** and **S6**, there is some noticeable change in the interference distribution (**Figures 10**–**12**), as the spectrum holes shift to the table's center. At the same time, the interference power has increased substantially, mainly in the observed area's periphery. Thus, when the interferers are within a very short distance between each other, it is much more difficult to diminish their influence,

6 [−2000, 0], [−2000, 1000], [0, 0], [0, 1000], [2000, 0], [2000, 1000] 9, 12, and 14 [−2800, 0], [−2800, 1000], [−1000, 0], [−1000, 1000], [1000, 0], [1000, 1000],

[2800, 0], [2800, 1000]

This chapter presents a spectrum occupancy evaluation for two popular IoT communication standards, LoRa and Wi-Fi, based on extensive experiments. These include the change of the interfering nodes' number and their location in dense indoor placement. The implementation is realized using the PlutoSDR hardware platform. The 3D interference maps show that the effect of fading with distance on the same plane and in height is crucial in localizing interference-free areas in dense deployments where even with the wireless standards' mechanisms for multiple accesses, it is likely that some nodes and/or malicious users will create in-band interference. In the case of LoRa in the 868 MHz band, interference is a much more substantial issue, regardless of the interferers' number and proximity. As for Wi-Fi sensors, due to the much higher carrier frequency, the interference's influence may be reduced substantially even within the span of a couple of meters. Thus, algorithms for adaptive repositioning in 3D have the potential for improving the communications of indoor IoT networks, aside from or in concurrency with future dynamic access techniques such as volumetric spectrum sensing [26]. Such methods can also be extended with Deep Learning-

based node identification for protection against physical layer attacks [27].

This work was supported by project No D-098-2019 "Monitoring and analysis of the spectrum occupancy and interference in ISM ranges for the implementation of

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