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Machine Learning Techniques for Wireless Time Series in the Context of Wireless Sensor Networks and IoT

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 7985

Special Issue Editors

Dr. Jaron Fontaine

E-Mail Website
Guest Editor
IDLab, Department of Information Technology, Ghent University-IMEC, 9052 Ghent, Belgium
Interests: wireless sensor networks; IoT; machine learning; zero-shot learning; TinyML
Prof. Dr. Eli De Poorter

E-Mail Website
Guest Editor
IDLab, Department of Information Technology, Ghent University-imec, Technologiepark-Zwijnaarde 126, B-9052 Gent, Belgium
Interests: wireless communication; indoor localisation; Internet of Things; machine learning for wireless networks; network protocols for low-power constrained devices
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adnan Shahid

E-Mail Website
Guest Editor
IDLab, Department of Information Technology, Ghent University-imec, Technologiepark-Zwijnaarde 126, B-9052 Gent, Belgium
Interests: machine learning and artificial intelligence for wireless communication and networks; 5G/xG; IoT; localization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Machine learning (ML) plays a pivotal role in the emergence of intelligent wireless sensors networks (WSN) and Internet of things (IoT). Within these domains, many wireless applications generate large heterogeneous datasets primarily in the form of wireless time series (e.g., in-phase and quadrature components (IQ), inertial measurement unit (IMU) samples, channel state information, channel impulse responses (CIRs), 3D positioning time series, etc.). These datasets can be used for diagnostics, optimization and application-level functionalities. ML allows the exploitation of these wireless time series to create intelligent, self-learning applications that adapt seamlessly to dynamic scenarios and diverse environments.

You are invited to contribute to this Special Issue, which aims to highlight the latest machine learning advancements in the field of wireless sensors networks. Topics include (but are not limited to) supervised and unsupervised ML, embedded TinyML, reinforcement learning, distributed ML, autoencoders, transformers, zero- and few-shot learning, meta-learning, and more. These techniques are suitable for various sensor applications, such as wireless network management and optimization, connected healthcare, wearable sensors, indoor localization systems, and industrial sensor networks. Your contributions will significantly contribute to ML, shaping the future of wireless sensor networks.

Dr. Jaron Fontaine
Prof. Dr. Eli De Poorter
Prof. Dr. Adnan Shahid
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine/deep learning
  • deep learning
  • reinforcement learning
  • unsupervised learning
  • transformers
  • TinyML
  • time series
  • wearable sensors
  • indoor localization
  • wireless networks
  • connected healthcare
  • network management

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Published Papers (4 papers)

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Research

27 pages, 1244 KiB  
Article
An Evolving Multivariate Time Series Compression Algorithm for IoT Applications
by Hagi Costa, Marianne Silva, Ignacio Sánchez-Gendriz, Carlos M. D. Viegas and Ivanovitch Silva
Sensors 2024, 24(22), 7273; https://doi.org/10.3390/s24227273 - 14 Nov 2024
Viewed by 410
Abstract
The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges [...] Read more.
The Internet of Things (IoT) is transforming how devices interact and share data, especially in areas like vehicle monitoring. However, transmitting large volumes of real-time data can result in high latency and substantial energy consumption. In this context, Tiny Machine Learning (TinyML) emerges as a promising solution, enabling the execution of machine-learning models on resource-constrained embedded devices. This paper aims to develop two online multivariate compression approaches specifically designed for TinyML, utilizing the Typicality and Eccentricity Data Analytics (TEDA) framework. The proposed approaches are based on data eccentricity and do not require predefined mathematical models or assumptions about data distribution, thereby optimizing compression performance. The methodology involves applying the approaches to a case study using the OBD-II Freematics ONE+ dataset, which is focused on vehicle monitoring. Results indicate that both proposed approaches, whether parallel or sequential compression, show significant improvements in execution time and compression errors. These findings highlight the approach’s potential to enhance the performance of embedded IoT systems, thereby improving the efficiency and sustainability of vehicular applications. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Wireless Time Series in the Context of Wireless Sensor Networks and IoT)
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23 pages, 17069 KiB  
Article
Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets
by Shoaib Sattar, Rafia Mumtaz, Mamoon Qadir, Sadaf Mumtaz, Muhammad Ajmal Khan, Timo De Waele, Eli De Poorter, Ingrid Moerman and Adnan Shahid
Sensors 2024, 24(8), 2484; https://doi.org/10.3390/s24082484 - 12 Apr 2024
Cited by 3 | Viewed by 3735
Abstract
ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset [...] Read more.
ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Wireless Time Series in the Context of Wireless Sensor Networks and IoT)
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22 pages, 6977 KiB  
Article
A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO2 Concentration as an Example
by Xiaoyang Chen, Lijia Yang, Hao Xue, Lihua Li and Yao Yu
Sensors 2024, 24(1), 244; https://doi.org/10.3390/s24010244 - 31 Dec 2023
Cited by 4 | Viewed by 1770
Abstract
In a layer house, the CO2 (carbon dioxide) concentration above the upper limit can cause the oxygen concentration to be below the lower limit suitable for poultry. This leads to chronic CO2 poisoning in layers, which manifests as listlessness, reduced appetite, [...] Read more.
In a layer house, the CO2 (carbon dioxide) concentration above the upper limit can cause the oxygen concentration to be below the lower limit suitable for poultry. This leads to chronic CO2 poisoning in layers, which manifests as listlessness, reduced appetite, weak constitution, decreased production performance, and weakened resistance to disease. Regulating ventilation may ensure a suitable CO2 concentration in layer houses. Predicting the changes in CO2 concentration and regulating the CO2 concentration in advance are key to ensuring healthy large-scale breeding of layers. In recent years, machine learning and deep learning methods have been increasingly applied to this field. A CO2 prediction model for layer house is proposed based on a GRU (gated recurrent unit) and LSTM (long short-term memory). The temperature, humidity, and CO2 were determined as inputs to the model by the correlation coefficient. The datasets of the experimental layer house were continuously measured during June–July 2023, using a self-developed environmental monitor, and the monitored data were used as samples for model inputs. There were 22,000 time series data in the datasets. In this study, multivariate time series data were standardized via data pre-processing to improve model training. GRU and LSTM models were constructed. The models were trained using a training set. Then, these trained models were used to provide predictions on a test set. The prediction errors were calculated using the true values of the test set and the predicted values provided by the models. To test the performance of the model and accuracy of the predictions, predictions were made for different numbers of datasets. The results demonstrated that the combined prediction model had good generalization, stability, and convergence with high prediction accuracy. Due to the structure of the model, the stability of the LSTM model was higher than that of the GRU model, and its prediction accuracy and speed were lower than those of the GRU model. When the datasets of the GRU model were 15,000 to 17,000, The MAE of the GRU was 70.8077 to 126.7029 ppm, and the prediction time of the GRU is 16 to 24 ms. When the LSTM model’s datasets were 15,000–20,000, the MAE of LSTM was 78.8596 to 136.0896 ppm, and the prediction time of the GRU was 17 to 26 ms. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Wireless Time Series in the Context of Wireless Sensor Networks and IoT)
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18 pages, 10439 KiB  
Article
Occupancy State Prediction by Recurrent Neural Network (LSTM): Multi-Room Context
by Mahamadou Klanan Diarra, Amine Maniar, Jean-Baptiste Masson, Bruno Marhic and Laurent Delahoche
Sensors 2023, 23(23), 9603; https://doi.org/10.3390/s23239603 - 4 Dec 2023
Cited by 1 | Viewed by 1305
Abstract
The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, [...] Read more.
The energy consumption of a building is significantly influenced by the habits of its occupants. These habits not only pertain to occupancy states, such as presence or absence, but also extend to more detailed aspects of occupant behavior. To accurately capture this information, it is essential to use tools that can monitor occupant habits without altering them. Invasive methods such as body sensors or cameras could potentially disrupt the natural habits of the occupants. In our study, we primarily focus on occupancy states as a representation of occupant habits. We have created a model based on artificial neural networks (ANNs) to ascertain the occupancy state of a building using environmental data such as CO2 concentration and noise level. These data are collected through non-intrusive sensors. Our approach involves rule-based a priori labeling and the use of a long short-term memory (LSTM) network for predictive purposes. The model is designed to predict four distinct states in a residential building. Although we lack data on actual occupancy states, the model has shown promising results with an overall prediction accuracy ranging between 78% and 92%. Full article
(This article belongs to the Special Issue Machine Learning Techniques for Wireless Time Series in the Context of Wireless Sensor Networks and IoT)
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