@article{maandafe:2026:futureinternet,

title = {Performance Assessment of DL for Network Intrusion Detection on a Constrained IoT Device},

author = {Armin Mazinani and Daniele Antonucci and Luca Davoli and Gianluigi Ferrari},

doi = {10.3390/fi18010034},

year  = {2026},

date = {2026-01-07},

urldate = {2026-01-01},

journal = {Future Internet},

volume = {18},

number = {1},

pages = {1-39},

abstract = {This work investigates the deployment of Deep Learning (DL) models for network intrusion detection on resource-constrained IoT devices, using the public CICIoT2023 dataset. In particular, we consider the following DL models: Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), Temporal Convolutional Network (TCN), Multi-Layer Perceptron (MLP). Bayesian optimization is employed to fine-tune the models’ hyperparameters and ensure reliable performance evaluation across both binary (2-class) and multi-class (8-class, 34-class) intrusion detection. Then, the computational complexity of each DL model is analyzed—in terms of the number of Multiply–ACCumulate operations (MACCs), RAM usage, and inference time—through the STMicroelectronics Cube.AI Analyzer tool, with models being deployed on an STM32H7S78-DK board. To assess the practical deployability of the considered DL models, a trade-off score (balancing classification accuracy and computational efficiency) is introduced: according to this score, our experimental results indicate that MLP and TCN outperform the other models. Furthermore, Post-Training Quantization (PTQ) to 8-bit integer precision is applied, allowing the model size to be reduced by more than 90% with negligible performance degradation. This demonstrates the effectiveness of quantization in optimizing DL models for real-world deployment on resource-constrained IoT devices.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{maanpadafe:2025:access,

title = {Air Quality Prediction via Embedded ML/DL and Quantized Models},

author = {Armin Mazinani and Daniele Antonucci and Danilo PIetro Pau and Luca Davoli and Gianluigi Ferrari},

doi = {10.1109/ACCESS.2025.3603920},

issn = {2169-3536},

year  = {2025},

date = {2025-08-28},

urldate = {2025-01-01},

journal = {IEEE Access},

volume = {13},

pages = {154203-154218},

abstract = {In the people’s everyday life, one of the most significant (and unfortunately well-known) environmental problems with substantial impact and effects is air pollution. To this end, extensive research (e.g., conducted by the World Health Organization, WHO) has uncovered a non-negligible correlation between hazardous environments and airborne Particulate Matters (PMs), emphasizing the importance of monitoring pollution to mitigate its adverse effects. In fact, since PMs are microscopic particles of solid or liquid materials suspended in the atmosphere and have negative effects on both weather and precipitations, it has been highlighted that the diffusion of diseases through the inhalation or absorption of contaminants is a concerning outcome of polluted environments. Hence, focusing on the critical need to monitor air quality in common (livable and work) environments (e.g., airports, public transports, or any closed environment) using affordable, compact IoT devices capable of collecting and processing environmental data, as well as forecasting future air quality trends, in this paper we present an experimental evaluation of several Machine Learning (ML), Deep Learning (DL), and quantized DL models designed to accurately predict air pollution (in terms of PM2.5 concentration). Furthermore, we conduct a comparative analysis of state-of-the-art DL models, unveiling the trade-offs associated with each ML/DL model, and discussing their practical applications and potential deployment on tiny IoT devices for on-site computing. Our results show that Convolutional Neural Network-Bidirectional Gated Recurrent Unit (CNN-BiGRU) is the best performing model. According to the obtained results, using 8-bit quantization on CNN-BiGRU allows to achieve a 66% model size’s reduction with only a 1% drop in average prediction accuracy, making it a balanced and efficient choice for several applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}