4.8
Impact Factor
11.3
CiteScore
25 days
Submission to First Decision
4 days
Acceptance to Publication

Journal of Sensor and Actuator Networks, Volume 12, Issue 6

2023 December - 4 articles

Cover Story: This study presents an efficient machine learning modeling designed to detect mental fatigue using physiological signals as key markers. Electrodermal Activity (EDA), Electrocardiogram (ECG), and respiration signals are integrated into a Random Forest (RF)-based model capable of classifying three levels of fatigue. To benchmark its efficacy, the RF was rigorously compared against other models. Diverging from conventional practices, we underscore the power of judicious feature selection. By meticulously choosing key features, the objective is not only to achieve high model performance but also to ensure reliability while reducing the feature count. View this paper

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21 Citations

9,542 Views

27 Pages

Performance Evaluation of LoRa Communications in Harsh Industrial Environments

L’houssaine Aarif,
Mohamed Tabaa and
Hanaa Hachimi

J. Sens. Actuator Netw.2023, 12(6), 80;https://doi.org/10.3390/jsan12060080

28 November 2023

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an ind...

(This article belongs to the Section Communications and Networking)

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Article

14 Citations

4,508 Views

18 Pages

Electric Vehicles Energy Management for Vehicle-to-Grid 6G-Based Smart Grid Networks

Rola Naja,
Aakash Soni and
Circe Carletti

J. Sens. Actuator Netw.2023, 12(6), 79;https://doi.org/10.3390/jsan12060079

27 November 2023

This research proposes a unique platform for energy management optimization in smart grids, based on 6G technologies. The proposed platform, applied on a virtual power plant, includes algorithms that take into account different profiles of loads and...

(This article belongs to the Special Issue Machine-Environment Interaction, Volume II)

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Article

8 Citations

4,825 Views

20 Pages

A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network

Giovanni Paragliola,
Patrizia Ribino and
Zaib Ullah

J. Sens. Actuator Netw.2023, 12(6), 78;https://doi.org/10.3390/jsan12060078

20 November 2023

A result of the pandemic is an urgent need for data collaborations that empower the clinical and scientific communities in responding to rapidly evolving global challenges. The ICU4Covid project joined research institutions, medical centers, and hosp...

(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)

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Article

20 Citations

7,186 Views

19 Pages

Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling

Carole-Anne Cos,
Alexandre Lambert,
Aakash Soni,
Haifa Jeridi,
Coralie Thieulin and
Amine Jaouadi

J. Sens. Actuator Netw.2023, 12(6), 77;https://doi.org/10.3390/jsan12060077

3 November 2023

This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, whi...

(This article belongs to the Special Issue Machine-Environment Interaction, Volume II)

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J. Sens. Actuator Netw. - ISSN 2224-2708

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