Page 10 - ITU Journal Future and evolving technologies Volume 2 (2021), Issue 5 – Internet of Everything

ITU Journal on Future and Evolving Technologies, Volume 2 (2021), Issue 5

From design to prototyping in the Internet of Things: A domotics case

study

Pages 29-37
Sabrina Sicari, Alessandra Rizzardi, Alberto Coen-Porisini
Nowadays, the capability of rapidly designing and prototyping, simple, yet real domotics systems (e.g.,
smart homes and smart buildings applications) is even more compelling, due to the availability and
increasing spread of Internet of Things (IoT) devices. Home automation services enable the remote
monitoring of indoor environments and facilities. The main advantages include saving energy
consumption and improving the overall management (and users' experience) in certain application
domains. The pervasive adoption and diffusion of such remote monitoring solutions is hampered by the
timing required for design, prototyping and further developing applications and underlying architecture,
which must be often customized on the basis of specific domains' needs and involved entities. To cope
with this issue, the paper proposes the analysis and prototyping of a domotics case study, in order to
demonstrate the effectiveness of proper IoT-related tools in speeding up the testing phase.

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RF-based low-SNR classification of UAVs using convolutional neural

networks

Pages 39-52
Ender Ozturk, Fatih Erden, Ismail Güvenç
Unmanned Aerial Vehicles (UAVs), or drones, which can be considered as a coverage extender for
Internet of Everything (IoE), have drawn high attention recently. The proliferation of drones will raise
privacy and security concerns in public. This paper investigates the problem of classification of drones
from Radio Frequency (RF) fingerprints at the low Signal-to-Noise Ratio (SNR) regime. We use
Convolutional Neural Networks (CNNs) trained with both RF time-series images and the spectrograms
of 15 different off-the-shelf drone controller RF signals. When using time-series signal images, the
CNN extracts features from the signal transient and envelope. As the SNR decreases, this approach fails
dramatically because the information in the transient is lost in the noise, and the envelope is distorted
heavily. In contrast to time-series representation of the RF signals, with spectrograms, it is possible to
focus only on the desired frequency interval, i.e., 2.4 GHz ISM band, and filter out any other signal
component outside of this band. These advantages provide a notable performance improvement over
the time-series signals-based methods. To further increase the classification accuracy of the
spectrogram-based CNN, we denoise the spectrogram images by truncating them to a limited spectral
density interval. Creating a single model using spectrogram images of noisy signals and tuning the CNN
model parameters, we achieve a classification accuracy varying from 92% to 100% for an SNR range
from -10 dB to 30 dB, which significantly outperforms the existing approaches to our best knowledge.
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