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

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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 11748

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Special Issue Editors

Prof. Dr. Vassilis Plagianakos

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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
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Dr. Sotiris Tasoulis

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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
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Dr. Spiros V. Georgakopoulos

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Department of Mathematics, University of Thessaly, Volos, Greece
Interests: machine learning; data mining; big data applications
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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
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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

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Keywords

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

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Published Papers (3 papers)

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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 19 | Viewed by 5925

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)

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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 2683

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)

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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 2418

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)

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