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Machine Learning in Wireless Sensor Networks
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Machine Learning in Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 6298

Special Issue Editors

Dr. Spiros V. Georgakopoulos

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Guest Editor
Department of Mathematics, University of Thessaly, Volos, Greece
Interests: machine learning; data mining; big data applications
Special Issues, Collections and Topics in MDPI journals
Dr. Sotiris Tasoulis

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Guest Editor
Intelligent Systems Lab of the Department of Computer Science and Biomedical Informatics, University of Thessaly, 382 21 Volos, Greece
Interests: machine learning; data mining; big data applications
Special Issues, Collections and Topics in MDPI journals
Dr. Parisis Gallos

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Guest Editor
Computational Biomedicine Research Lab, Department of Digital Systems, University of Piraeus, 185 34 Pireas, Greece
Interests: m-health; technology assessment; statistical data analysis
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vassilis Plagianakos

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Guest Editor
Intelligent Systems Lab of the Department of Computer Science and Biomedical Informatics, University of Thessaly, 382 21 Volos, Greece
Interests: theory of neural networks and learning; evolutionary and genetic algorithms; machine learning applications in pattern recognition; biomedical informatics and bioinformatics; data mining and big data analysis; intelligent decision making; parallel and distributed computations; intelligent optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Wireless sensor networks (WSNs) consist of spatially dispersed and dedicated sensors that are used to monitor and record systems’ physical or environmental conditions. As a result, a high number and wide variety of data are collected. Meanwhile, in machine learning research there is an increasing interest in developing algorithms on embedded devices in an attempt to tackle device limitations.

This Special Issue highlights developments in machine learning methodologies able to tackle the various challenges arising when dealing with WSNs. The Issue accepts both high-quality articles containing original research results as well as review articles. It will allow readers to learn more about the potential of machine learning applications in WSNs.

Dr. Spiros V. Georgakopoulos
Dr. Sotiris Tasoulis
Dr. Parisis Gallos
Prof. Dr. Vassilis Plagianakos
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 learning
  • wireless sensor networks
  • deep learning
  • data streams
  • Internet of Things
  • sensor data
  • intelligent systems

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Published Papers (2 papers)

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15 pages, 1126 KiB  
Article
Deep-Reinforcement-Learning-Based IoT Sensor Data Cleaning Framework for Enhanced Data Analytics
by Alaelddin F. Y. Mohammed, Salman Md Sultan, Joohyung Lee and Sunhwan Lim
Sensors 2023, 23(4), 1791; https://doi.org/10.3390/s23041791 - 5 Feb 2023
Cited by 7 | Viewed by 3779
Abstract
The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from [...] Read more.
The Internet of things (IoT) combines different sources of collected data which are processed and analyzed to support smart city applications. Machine learning and deep learning algorithms play a vital role in edge intelligence by minimizing the amount of irrelevant data collected from multiple sources to facilitate these smart city applications. However, the data collected by IoT sensors can often be noisy, redundant, and even empty, which can negatively impact the performance of these algorithms. To address this issue, it is essential to develop effective methods for detecting and eliminating irrelevant data to improve the performance of intelligent IoT applications. One approach to achieving this goal is using data cleaning techniques, which can help identify and remove noisy, redundant, or empty data from the collected sensor data. This paper proposes a deep reinforcement learning (deep RL) framework for IoT sensor data cleaning. The proposed system utilizes a deep Q-network (DQN) agent to classify sensor data into three categories: empty, garbage, and normal. The DQN agent receives input from three received signal strength (RSS) values, indicating the current and two previous sensor data points, and receives reward feedback based on its predicted actions. Our experiments demonstrate that the proposed system outperforms a common time-series-based fully connected neural network (FCDQN) solution, with an accuracy of around 96% after the exploration mode. The use of deep RL for IoT sensor data cleaning is significant because it has the potential to improve the performance of intelligent IoT applications by eliminating irrelevant and harmful data. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks)
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21 pages, 791 KiB  
Article
Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence
by Kyoung-Sook Moon, Hee Won Lee and Hongjoong Kim
Sensors 2022, 22(20), 7982; https://doi.org/10.3390/s22207982 - 19 Oct 2022
Cited by 3 | Viewed by 1973
Abstract
Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm [...] Read more.
Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms. Full article
(This article belongs to the Special Issue Machine Learning in Wireless Sensor Networks)
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