Submit to Sensors Review for Sensors Propose a Special Issue

Journal Menu

Journal Browser

► Journal Browser

Internet of Things (IoT) and Neural Networks in Smart Sensor Environments

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

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

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 10841

Share This Special Issue

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, 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

Dr. Spiros V. Georgakopoulos

E-Mail Website
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. Parisis Gallos

E-Mail Website
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

Special Issue Information

Dear Colleagues,

We are living in the era of smart sensors, the Internet of Things (IoT) and Ambient Intelligence (AmI). Sensors and embedded devices are omnipresent, adapting the environment to each individual’s preferences. The development of novel Machine Learning and Neural Network algorithms is crucial to tackle many open problems, especially in object detection and human identification.

This Special Issue aims to highlight developments in Neural Network methodologies able to be applied to the various challenges emerging in the fields of IoT and smart environments. We welcome both high-quality original research results and review articles that further readers’ understanding of the potential synergy of Neural Networks and IoT.

Prof. Dr. Vassilis Plagianakos
Dr. Sotiris Tasoulis
Dr. Spiros V. Georgakopoulos
Dr. Parisis Gallos
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

Deep Learning
Neural Networks
data streams
Internet of Things
sensor data
intelligent systems
Ambient Intelligence

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

21 pages, 1993 KiB

Open AccessArticle

Real-Time Anomaly Detection for Water Quality Sensor Monitoring Based on Multivariate Deep Learning Technique

by Engy El-Shafeiy, Maazen Alsabaan, Mohamed I. Ibrahem and Haitham Elwahsh

Sensors 2023, 23(20), 8613; https://doi.org/10.3390/s23208613 - 20 Oct 2023

Cited by 17 | Viewed by 5366

Abstract

With the increased use of automated systems, the Internet of Things (IoT), and sensors for real-time water quality monitoring, there is a greater requirement for the timely detection of unexpected values. Technical faults can introduce anomalies, and a large incoming data rate might make the manual detection of erroneous data difficult. This research introduces and applies a pioneering technology, Multivariate Multiple Convolutional Networks with Long Short-Term Memory (MCN-LSTM), to real-time water quality monitoring. MCN-LSTM is a cutting-edge deep learning technology designed to address the difficulty of detecting anomalies in complicated time series data, particularly in monitoring water quality in a real-world setting. The growing reliance on automated systems, the Internet of Things (IoT), and sensor networks for continuous water quality monitoring is driving the development and deployment of the MCN-LSTM approach. As these technologies become more widely used, the rapid and precise identification of unexpected or aberrant data points becomes critical. Technical difficulties, inherent noise, and a high data influx pose significant hurdles to manual anomaly detection processes. The MCN-LSTM technique takes advantage of deep learning by integrating Multiple Convolutional Networks and Long Short-Term Memory networks. This combination of approaches offers efficient and effective anomaly detection in multivariate time series data, allowing for identifying and flagging unexpected patterns or values that may signal water quality issues. Water quality data anomalies can have far-reaching repercussions, influencing future analyses and leading to incorrect judgments. Anomaly identification must be precise to avoid inaccurate findings and ensure the integrity of water quality tests. Extensive tests were carried out to validate the MCN-LSTM technique utilizing real-world information obtained from sensors installed in water quality monitoring scenarios. The results of these studies proved MCN-LSTM’s outstanding efficacy, with an impressive accuracy rate of 92.3%. This high level of precision demonstrates the technique’s capacity to discriminate between normal and abnormal data instances in real time. The MCN-LSTM technique is a big step forward in water quality monitoring. It can improve decision-making processes and reduce adverse outcomes caused by undetected abnormalities. This unique technique has significant promise for defending human health and maintaining the environment in an era of increased reliance on automated monitoring systems and IoT technology by contributing to the safety and sustainability of water supplies. Full article

(This article belongs to the Special Issue Internet of Things (IoT) and Neural Networks in Smart Sensor Environments)

► Show Figures

Figure 1

21 pages, 7940 KiB

Open AccessArticle

Advanced IoT Pressure Monitoring System for Real-Time Landfill Gas Management

by Cormac D. Fay, John P. Healy and Dermot Diamond

Sensors 2023, 23(17), 7574; https://doi.org/10.3390/s23177574 - 31 Aug 2023

Cited by 4 | Viewed by 2472

Abstract

This research presents a novel stand-alone device for the autonomous measurement of gas pressure levels on an active landfill site, which enables the real-time monitoring of gas dynamics and supports the early detection of critical events. The developed device employs advanced sensing technologies and wireless communication capabilities, enabling remote data transmission and access via the Internet. Through extensive field experiments, we demonstrate the high sampling rate of the device and its ability to detect significant events related to gas generation dynamics in landfills, such as flare shutdowns or blockages that could lead to hazardous conditions. The validation of the device’s performance against a high-end analytical system provides further evidence of its reliability and accuracy. The developed technology herein offers a cost-effective and scalable solution for environmental landfill gas monitoring and management. We expect that this research will contribute to the advancement of environmental monitoring technologies and facilitate better decision-making processes for sustainable waste management. Full article

(This article belongs to the Special Issue Internet of Things (IoT) and Neural Networks in Smart Sensor Environments)

► Show Figures

Figure 1

23 pages, 1141 KiB

Open AccessArticle

A Semi-Supervised Approach for Improving Generalization in Non-Intrusive Load Monitoring

by Dea Pujić, Nikola Tomašević and Marko Batić

Sensors 2023, 23(3), 1444; https://doi.org/10.3390/s23031444 - 28 Jan 2023

Cited by 9 | Viewed by 2294

Abstract

Non-intrusive load monitoring (NILM) considers different approaches for disaggregating energy consumption in residential, tertiary, and industrial buildings to enable smart grid services. The main feature of NILM is that it can break down the bulk electricity demand, as recorded by conventional smart meters, into the consumption of individual appliances without the need for additional meters or sensors. Furthermore, NILM can identify when an appliance is in use and estimate its real-time consumption based on its unique consumption patterns. However, NILM is based on machine learning methods and its performance is dependent on the quality of the training data for each appliance. Therefore, a common problem with NILM systems is that they may not generalize well to new environments where the appliances are unknown, which hinders their widespread adoption and more significant contributions to emerging smart grid services. The main goal of the presented research is to apply a domain adversarial neural network (DANN) approach to improve the generalization of NILM systems. The proposed semi-supervised algorithm utilizes both labeled and unlabeled data and was tested on data from publicly available REDD and UK-DALE datasets. The results show a

3 %

improvement in generalization performance on highly uncorrelated data, indicating the potential for real-world applications. Full article

(This article belongs to the Special Issue Internet of Things (IoT) and Neural Networks in Smart Sensor Environments)

► Show Figures

Journal Menu

Journal Browser

Internet of Things (IoT) and Neural Networks in Smart Sensor Environments

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (3 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI