Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
Machine Learning Applied to Sensor Data Analysis
sensors-logo
Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Machine Learning Applied to Sensor Data Analysis

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

Deadline for manuscript submissions: closed (30 October 2021) | Viewed by 26897

Special Issue Editors

Prof. Dr. Vassilis Plagianakos

E-Mail Website
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
Dr. Sotiris Tasoulis

E-Mail Website
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

Special Issue Information

Dear Colleagues,

As hardware is becoming smaller and sensors are getting cheaper, there is an increasing interest in how to effectively analyze huge collections of sensor data. Meanwhile, the emergence of machine learning has led to applications, which have a direct impact in our lives. In an attempt to provide accurate, in some occasions real-time, predictions even for noisy sensor datasets, machine learning models are widely implemented.

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

Prof. Dr. Vassilis Plagianakos
Dr. Sotiris Tasoulis
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
  • deep learning
  • big data
  • data streams
  • Internet of Things
  • sensor data
  • intelligent systems

Published Papers (6 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Other

18 pages, 3145 KiB  
Article
Anomaly Detection of Water Level Using Deep Autoencoder
by Isack Thomas Nicholaus, Jun Ryeol Park, Kyuil Jung, Jun Seoung Lee and Dae-Ki Kang
Sensors 2021, 21(19), 6679; https://0-doi-org.brum.beds.ac.uk/10.3390/s21196679 - 08 Oct 2021
Cited by 14 | Viewed by 5766
Abstract
Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of [...] Read more.
Anomaly detection is one of the crucial tasks in daily infrastructure operations as it can prevent massive damage to devices or resources, which may then lead to catastrophic outcomes. To address this challenge, we propose an automated solution to detect anomaly pattern(s) of the water levels and report the analysis and time/point(s) of abnormality. This research’s motivation is the level difficulty and time-consuming managing facilities responsible for controlling water levels due to the rare occurrence of abnormal patterns. Consequently, we employed deep autoencoder, one of the types of artificial neural network architectures, to learn different patterns from the given sequences of data points and reconstruct them. Then we use the reconstructed patterns from the deep autoencoder together with a threshold to report which patterns are abnormal from the normal ones. We used a stream of time-series data collected from sensors to train the model and then evaluate it, ready for deployment as the anomaly detection system framework. We run extensive experiments on sensor data from water tanks. Our analysis shows why we conclude vanilla deep autoencoder as the most effective solution in this scenario. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
►▼ Show Figures

Figure 1

22 pages, 2801 KiB  
Article
Driving Behaviour Analysis Using Machine and Deep Learning Methods for Continuous Streams of Vehicular Data
by Nikolaos Peppes, Theodoros Alexakis, Evgenia Adamopoulou and Konstantinos Demestichas
Sensors 2021, 21(14), 4704; https://0-doi-org.brum.beds.ac.uk/10.3390/s21144704 - 09 Jul 2021
Cited by 19 | Viewed by 7230
Abstract
In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together [...] Read more.
In the last few decades, vehicles are equipped with a plethora of sensors which can provide useful measurements and diagnostics for both the vehicle’s condition as well as the driver’s behaviour. Furthermore, the rapid increase for transportation needs of people and goods together with the evolution of Information and Communication Technologies (ICT) push the transportation domain towards a new more intelligent and efficient era. The reduction of CO2 emissions and the minimization of the environmental footprint is, undeniably, of utmost importance for the protection of the environment. In this light, it is widely acceptable that the driving behaviour is directly associated with the vehicle’s fuel consumption and gas emissions. Thus, given the fact that, nowadays, vehicles are equipped with sensors that can collect a variety of data, such as speed, acceleration, fuel consumption, direction, etc. is more feasible than ever to put forward solutions which aim not only to monitor but also improve the drivers’ behaviour from an environmental point of view. The approach presented in this paper describes a holistic integrated platform which combines well-known machine and deep learning algorithms together with open-source-based tools in order to gather, store, process, analyze and correlate different data flows originating from vehicles. Particularly, data streamed from different vehicles are processed and analyzed with the utilization of clustering techniques in order to classify the driver’s behaviour as eco-friendly or not, followed by a comparative analysis of supervised machine and deep learning algorithms in the given labelled dataset. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
►▼ Show Figures

Figure 1

18 pages, 2020 KiB  
Article
Identifying and Estimating the Location of Sources of Industrial Pollution in the Sewage Network
by Magdalena Paulina Buras and Fernando Solano Donado
Sensors 2021, 21(10), 3426; https://0-doi-org.brum.beds.ac.uk/10.3390/s21103426 - 14 May 2021
Cited by 10 | Viewed by 2501
Abstract
Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the [...] Read more.
Harsh pollutants that are illegally disposed in the sewer network may spread beyond the sewer network—e.g., through leakages leading to groundwater reservoirs—and may also impair the correct operation of wastewater treatment plants. Consequently, such pollutants pose serious threats to water bodies, to the natural environment and, therefore, to all life. In this article, we focus on the problem of identifying a wastewater pollutant and localizing its source point in the wastewater network, given a time-series of wastewater measurements collected by sensors positioned across the sewer network. We provide a solution to the problem by solving two linked sub-problems. The first sub-problem concerns the detection and identification of the flowing pollutants in wastewater, i.e., assessing whether a given time-series corresponds to a contamination event and determining what the polluting substance caused it. This problem is solved using random forest classifiers. The second sub-problem relates to the estimation of the distance between the point of measurement and the pollutant source, when considering the outcome of substance identification sub-problem. The XGBoost algorithm is used to predict the distance from the source to the sensor. Both of the models are trained using simulated electrical conductivity and pH measurements of wastewater in sewers of a european city sub-catchment area. Our experiments show that: (a) resulting precision and recall values of the solution to the identification sub-problem can be both as high as 96%, and that (b) the median of the error that is obtained for the estimation of the source location sub-problem can be as low as 6.30 m. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
►▼ Show Figures

Figure 1

20 pages, 5603 KiB  
Article
Distributed Spectrum Management in Cognitive Radio Networks by Consensus-Based Reinforcement Learning
by Dejan Dašić, Nemanja Ilić, Miljan Vučetić, Miroslav Perić, Marko Beko and Miloš S. Stanković
Sensors 2021, 21(9), 2970; https://0-doi-org.brum.beds.ac.uk/10.3390/s21092970 - 23 Apr 2021
Cited by 7 | Viewed by 2329
Abstract
In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth [...] Read more.
In this paper, we propose a new algorithm for distributed spectrum sensing and channel selection in cognitive radio networks based on consensus. The algorithm operates within a multi-agent reinforcement learning scheme. The proposed consensus strategy, implemented over a directed, typically sparse, time-varying low-bandwidth communication network, enforces collaboration between the agents in a completely decentralized and distributed way. The motivation for the proposed approach comes directly from typical cognitive radio networks’ practical scenarios, where such a decentralized setting and distributed operation is of essential importance. Specifically, the proposed setting provides all the agents, in unknown environmental and application conditions, with viable network-wide information. Hence, a set of participating agents becomes capable of successful calculation of the optimal joint spectrum sensing and channel selection strategy even if the individual agents are not. The proposed algorithm is, by its nature, scalable and robust to node and link failures. The paper presents a detailed discussion and analysis of the algorithm’s characteristics, including the effects of denoising, the possibility of organizing coordinated actions, and the convergence rate improvement induced by the consensus scheme. The results of extensive simulations demonstrate the high effectiveness of the proposed algorithm, and that its behavior is close to the centralized scheme even in the case of sparse neighbor-based inter-node communication. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
►▼ Show Figures

Figure 1

17 pages, 3056 KiB  
Article
Evolutionary Algorithm for Improving Decision Tree with Global Discretization in Manufacturing
by Sungbum Jun
Sensors 2021, 21(8), 2849; https://0-doi-org.brum.beds.ac.uk/10.3390/s21082849 - 18 Apr 2021
Cited by 7 | Viewed by 2349
Abstract
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, [...] Read more.
Due to the recent advance in the industrial Internet of Things (IoT) in manufacturing, the vast amount of data from sensors has triggered the need for leveraging such big data for fault detection. In particular, interpretable machine learning techniques, such as tree-based algorithms, have drawn attention to the need to implement reliable manufacturing systems, and identify the root causes of faults. However, despite the high interpretability of decision trees, tree-based models make a trade-off between accuracy and interpretability. In order to improve the tree’s performance while maintaining its interpretability, an evolutionary algorithm for discretization of multiple attributes, called Decision tree Improved by Multiple sPLits with Evolutionary algorithm for Discretization (DIMPLED), is proposed. The experimental results with two real-world datasets from sensors showed that the decision tree improved by DIMPLED outperformed the performances of single-decision-tree models (C4.5 and CART) that are widely used in practice, and it proved competitive compared to the ensemble methods, which have multiple decision trees. Even though the ensemble methods could produce slightly better performances, the proposed DIMPLED has a more interpretable structure, while maintaining an appropriate performance level. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
►▼ Show Figures

Figure 1

Other

Jump to: Research

13 pages, 260 KiB  
Perspective
Are Machine Learning Methods the Future for Smoking Cessation Apps?
by Maryam Abo-Tabik, Yael Benn and Nicholas Costen
Sensors 2021, 21(13), 4254; https://0-doi-org.brum.beds.ac.uk/10.3390/s21134254 - 22 Jun 2021
Cited by 1 | Viewed by 2862
Abstract
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support [...] Read more.
Smoking cessation apps provide efficient, low-cost and accessible support to smokers who are trying to quit smoking. This article focuses on how up-to-date machine learning algorithms, combined with the improvement of mobile phone technology, can enhance our understanding of smoking behaviour and support the development of advanced smoking cessation apps. In particular, we focus on the pros and cons of existing approaches that have been used in the design of smoking cessation apps to date, highlighting the need to improve the performance of these apps by minimizing reliance on self-reporting of environmental conditions (e.g., location), craving status and/or smoking events as a method of data collection. Lastly, we propose that making use of more advanced machine learning methods while enabling the processing of information about the user’s circumstances in real time is likely to result in dramatic improvement in our understanding of smoking behaviour, while also increasing the effectiveness and ease-of-use of smoking cessation apps, by enabling the provision of timely, targeted and personalised intervention. Full article
(This article belongs to the Special Issue Machine Learning Applied to Sensor Data Analysis)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-6
Back to TopTop