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J. Sens. Actuator Netw., Volume 12, Issue 6 (December 2023) – 4 articles

Cover Story (view full-size image): 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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27 pages, 7028 KiB  
Article
Performance Evaluation of LoRa Communications in Harsh Industrial Environments
J. Sens. Actuator Netw. 2023, 12(6), 80; https://doi.org/10.3390/jsan12060080 - 28 Nov 2023
Cited by 1 | Viewed by 1397
Abstract
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 industrial environment, necessitating solutions [...] Read more.
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 industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters. Full article
(This article belongs to the Section Communications and Networking)
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18 pages, 618 KiB  
Article
Electric Vehicles Energy Management for Vehicle-to-Grid 6G-Based Smart Grid Networks
J. Sens. Actuator Netw. 2023, 12(6), 79; https://doi.org/10.3390/jsan12060079 - 27 Nov 2023
Viewed by 1295
Abstract
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 fairly schedules energy according to loads [...] Read more.
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 fairly schedules energy according to loads priorities and compensates for the intermittent nature of renewable energy sources. Moreover, we develop a bidirectional energy transition mechanism towards a fleet of intelligent vehicles by adopting vehicle-to-grid technology and peak clipping. Performance analysis shows that the proposed energy provides fairness to electrical vehicles, satisfies urgent loads, and optimizes smart grids energy. Full article
(This article belongs to the Special Issue Machine-Environment Interaction, Volume II)
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20 pages, 5774 KiB  
Article
A Federated Learning Approach to Support the Decision-Making Process for ICU Patients in a European Telemedicine Network
J. Sens. Actuator Netw. 2023, 12(6), 78; https://doi.org/10.3390/jsan12060078 - 20 Nov 2023
Viewed by 1105
Abstract
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 hospitals all around Europe in a telemedicine network [...] Read more.
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 hospitals all around Europe in a telemedicine network for sharing capabilities, knowledge, and expertise distributed within the network. However, healthcare data sharing has ethical, regulatory, and legal complexities that pose several restrictions on their access and use. To mitigate this issue, the ICU4Covid project integrates a federated learning architecture, allowing distributed machine learning within a cross-institutional healthcare system without the data being transported or exposed outside their original location. This paper presents the federated learning approach to support the decision-making process for ICU patients in a European telemedicine network. The proposed approach was applied to the early identification of high-risk hypertensive patients. Experimental results show how the knowledge of every single node is spread within the federation, improving the ability of each node to make an early prediction of high-risk hypertensive patients. Moreover, a performance evaluation shows an accuracy and precision of over 90%, confirming a good performance of the FL approach as a prediction test. The FL approach can significantly support the decision-making process for ICU patients in distributed networks of federated healthcare organizations. Full article
(This article belongs to the Special Issue Federated Learning: Applications and Future Directions)
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19 pages, 4711 KiB  
Article
Enhancing Mental Fatigue Detection through Physiological Signals and Machine Learning Using Contextual Insights and Efficient Modelling
J. Sens. Actuator Netw. 2023, 12(6), 77; https://doi.org/10.3390/jsan12060077 - 03 Nov 2023
Viewed by 1140
Abstract
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, while emphasizing the significant contribution of the feature selection process, resulting in, not only [...] Read more.
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, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue. Full article
(This article belongs to the Special Issue Machine-Environment Interaction, Volume II)
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