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

Deadline for manuscript submissions: closed (20 February 2020) | Viewed by 72076

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Special Issue Editors

Dr. Jörg Gebhardt

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Guest Editor

ABB Corporate Research Center Germany, Wallstadter Str. 59, DE-68526 Ladenburg, Germany
Interests: sensors; system simulation; instrumentation; flow; temperature; industrial digitization

Dr. Martin Gilje Jaatun

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Co-Guest Editor

Software Engineering, Safety and Security Department, SINTEF, NO-7465 Trondheim, Norway
Interests: software security; cloud security; critical infrastructure security; smartgrid security
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Valentina E. Balas

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Co-Guest Editor

Department of Automatics and Applied Software, Faculty of Engineering, Aurel Vlaicu University of Arad, Ro-310025 Arad, Romania
Interests: neuro-fuzzy technologies; fuzzy logic approaches; adaptive fuzzy
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Dr. Thomas Strasser

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Co-Guest Editor

Electric Energy Systems, Center for Energy, AIT Austrian Institute of Technology, Giefinggasse 2, A-1210 Vienna, Austria
Interests: power utility automation; modelling and (real-time) simulation of smart grid systems; ICT for smart grids; validation and testing of smart grid systems; hardware-in-the-loop experiments
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Special Issue Information

Dear Colleagues,

This Special Issue primarily aims at soliciting selected papers presented at the SA2019—First International Conference on Societal Automation, held on 4–6 September 2019 in Krakow, Poland. We invite all contributors to submit an extended version of their SA2019 contribution in this Special Issue of the journal Sensors.

In addition to the SA2019 papers, other independent submissions are also welcome. The theme of these contributions may involve any aspect pertaining to the area of Sensing in Societal Automation.

Sensor technology is at the basis of many ideas and developments in digitization and Industry 4.0. The availability of specific sensing methods to a sufficient accuracy is often decisive for the feasibility of automation concepts. Sensing defines the way automation systems collect information from their environment—and often from us, as users. Future sensing solutions will be embedded in large automation systems, such as smart buildings, factories, and cities. Therefore, first, it is of great interest to have a look at visions of future automation solutions and find out which—perhaps qualitatively new—sensing solutions will be needed for that. Vice versa, recent rapid progress in sensor development inspires us to think about previously unthinkable automation.

Topics of interest include but are not limited to the following:

Unconventional sensing;
Non-invasive and remote sensing;
Pervasive sensing;
Model-based and soft sensing;
Novel components;
Innovative applications and deployment experiences;
System modeling, simulation, measurements, and analysis.

In sensor networks:

Devices and architectures for networked sensing;
Network and system architectures and protocols;
Network health monitoring, management, and accuracy considerations;
Sensor tasking, control, and actuation.

In sensors for digitization:

Energy harvested systems;
Transparent plants/factories and privacy;
Influence factors from and impact on workforce and labor;
Detection, classification, tracking, reasoning, and decision making;
Sensors for condition monitoring, self-diagnostics and self-healing systems, instrument- overlapping diagnostics;
Sensors and plant autonomy.

And in signal processing:

Distributed signal processing;
Fusion of sensor information;
Sensing and artificial intelligence;
Sensor data processing, mining, and machine learning;
Feature engineering and data compression.

Dr. Jörg Gebhardt
Dr. Martin Gilje Jaatun
Prof. Dr. Valentina Emilia Balas
Dr. Thomas Strasser
Guest Editor

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

Unconventional sensing
Model-based, remote, and non-invasive sensing
Sensor networks
Sensors for industrial digitization and Industry 4.0
Signal processing

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Published Papers (7 papers)

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Research

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10 pages, 5385 KiB

Open AccessArticle

Modeling and Piezoelectric Analysis of Nano Energy Harvesters

by Muhammad Faisal Wasim, Shahzadi Tayyaba, Muhammad Waseem Ashraf and Zubair Ahmad

Sensors 2020, 20(14), 3931; https://doi.org/10.3390/s20143931 - 15 Jul 2020

Cited by 8 | Viewed by 3861

Abstract

The expedient way for the development of microelectromechanical systems (MEMS) based devices are based on two key steps. First, perform the simulation for the optimization of various parameters by using different simulation tools that lead to cost reduction. Second, develop the devices with accurate fabrication steps using optimized parameters. Here, authors have performed a piezoelectric analysis of an array of zinc oxide (ZnO) nanostructures that have been created on both sides of aluminum sheets. Various quantities like swerve, stress, strain, electric flux, energy distribution, and electric potential have been studied during the piezo analysis. Then actual controlled growth of ZnO nanorods (NRs) arrays was done on both sides of the etched aluminum rod at low-temperature using the chemical bath deposition (CBD) method for the development of a MEMS energy harvester. Micro creaks on the substrate acted as an alternative to the seed layer. The testing was performed by applying ambient range force on the nanostructure. It was found that the voltage range on topside was 0.59 to 0.62 mV, and the bottom side was 0.52 to 0.55 mV. These kinds of devices are useful in low power micro-devices, nanoelectromechanical systems, and smart wearable systems. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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37 pages, 1971 KiB

Open AccessArticle

The Use of Ensemble Models for Multiple Class and Binary Class Classification for Improving Intrusion Detection Systems

by Celestine Iwendi, Suleman Khan, Joseph Henry Anajemba, Mohit Mittal, Mamdouh Alenezi and Mamoun Alazab

Sensors 2020, 20(9), 2559; https://doi.org/10.3390/s20092559 - 30 Apr 2020

Cited by 117 | Viewed by 7586

Abstract

The pursuit to spot abnormal behaviors in and out of a network system is what led to a system known as intrusion detection systems for soft computing besides many researchers have applied machine learning around this area. Obviously, a single classifier alone in the classifications seems impossible to control network intruders. This limitation is what led us to perform dimensionality reduction by means of correlation-based feature selection approach (CFS approach) in addition to a refined ensemble model. The paper aims to improve the Intrusion Detection System (IDS) by proposing a CFS + Ensemble Classifiers (Bagging and Adaboost) which has high accuracy, high packet detection rate, and low false alarm rate. Machine Learning Ensemble Models with base classifiers (J48, Random Forest, and Reptree) were built. Binary classification, as well as Multiclass classification for KDD99 and NSLKDD datasets, was done while all the attacks were named as an anomaly and normal traffic. Class labels consisted of five major attacks, namely Denial of Service (DoS), Probe, User-to-Root (U2R), Root to Local attacks (R2L), and Normal class attacks. Results from the experiment showed that our proposed model produces 0 false alarm rate (FAR) and 99.90% detection rate (DR) for the KDD99 dataset, and 0.5% FAR and 98.60% DR for NSLKDD dataset when working with 6 and 13 selected features. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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17 pages, 1803 KiB

Open AccessArticle

A Multi-task Learning Model for Daily Activity Forecast in Smart Home

by Hong Yang, Shanshan Gong, Yaqing Liu, Zhengkui Lin and Yi Qu

Sensors 2020, 20(7), 1933; https://doi.org/10.3390/s20071933 - 30 Mar 2020

Cited by 15 | Viewed by 4507

Abstract

Daily activity forecasts play an important role in the daily lives of residents in smart homes. Category forecasts and occurrence time forecasts of daily activity are two key tasks. Category forecasts of daily activity are correlated with occurrence time forecasts, however, existing research has only focused on one of the two tasks. Moreover, the performance of daily activity forecasts is low when the two tasks are performed in series. In this paper, a forecast model based on multi-task learning is proposed to forecast category and occurrence time of daily activity mutually and iteratively. Firstly, raw sensor events are pre-processed to form a feature space of daily activity. Secondly, a parallel multi-task learning model which combines a convolutional neural network (CNN) with bidirectional long short-term memory (Bi-LSTM) units are developed as the forecast model. Finally, five distinct datasets are used to evaluate the proposed model. The experimental results show that compared with the state-of-the-art single-task learning models, this model improves accuracy by at least 2.22%, and the metrics of NMAE, NRMSE and R² are improved by at least 1.542%, 7.79% and 1.69%, respectively. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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21 pages, 7085 KiB

Open AccessArticle

IoT Smart Parking System Based on the Visual-Aided Smart Vehicle Presence Sensor: SPIN-V

by Luis F. Luque-Vega, David A. Michel-Torres, Emmanuel Lopez-Neri, Miriam A. Carlos-Mancilla and Luis E. González-Jiménez

Sensors 2020, 20(5), 1476; https://doi.org/10.3390/s20051476 - 8 Mar 2020

Cited by 58 | Viewed by 32595

Abstract

Humanity is currently experiencing one of the short periods of transition thanks to novel sensing solutions for smart cities that bring the future to today. Overpopulation of cities demands the development of solid strategic plannings that uses infrastructure, innovation, and technology to adapt to rapid changes. To improve mobility in cities with a larger and larger vehicle fleet, a novel sensing solution that is the cornerstone of a smart parking system, the smart vehicular presence sensor (SPIN-V, in its Spanish abbreviation), is presented. The SPIN-V is composed of a small single-board computer, distance sensor, camera, LED indicator, buzzer, and battery and devoted to obtain the status of a parking space. This smart mobility project involves three main elements, namely the SPIN-V, a mobile application, and a monitoring center, working together to monitor, control, process, and display the parking space information in real-time to the drivers. In addition, the design and implementation of the three elements of the complete architecture are presented. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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15 pages, 1658 KiB

Open AccessArticle

Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering

by Jinghuan Guo, Yiming Li, Mengnan Hou, Shuo Han and Jianxun Ren

Sensors 2020, 20(5), 1457; https://doi.org/10.3390/s20051457 - 6 Mar 2020

Cited by 23 | Viewed by 3608

Abstract

With the development of population aging, the recognition of elderly activity in smart homes has received increasing attention. In recent years, single-resident activity recognition based on smart homes has made great progress. However, few researchers have focused on multi-resident activity recognition. In this paper, we propose a method to recognize two-resident activities based on time clustering. First, to use a de-noising method to extract the feature of the dataset. Second, to cluster the dataset based on the begin time and end time. Finally, to complete activity recognition using a similarity matching method. To test the performance of the method, we used two two-resident datasets provided by Center for Advanced Studies in Adaptive Systems (CASAS). We evaluated our method by comparing it with some common classifiers. The results show that our method has certain improvements in the accuracy, recall, precision, and F-Measure. At the end of the paper, we explain the parameter selection and summarize our method. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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36 pages, 5869 KiB

Open AccessArticle

Prioritization of Information Security Controls through Fuzzy AHP for Cloud Computing Networks and Wireless Sensor Networks

by Muhammad Imran Tariq, Shakeel Ahmed, Nisar Ahmed Memon, Shahzadi Tayyaba, Muhammad Waseem Ashraf, Mohsin Nazir, Akhtar Hussain, Valentina Emilia Balas and Marius M. Balas

Sensors 2020, 20(5), 1310; https://doi.org/10.3390/s20051310 - 28 Feb 2020

Cited by 31 | Viewed by 6248

Abstract

With the advent of cloud computing and wireless sensor networks, the number of cyberattacks has rapidly increased. Therefore, the proportionate security of networks has become a challenge for organizations. Information security advisors of organizations face difficult and complex decisions in the evaluation and selection of information security controls that permit the defense of their resources and assets. Information security controls must be selected based on an appropriate level of security. However, their selection needs intensive investigation regarding vulnerabilities, risks, and threats prevailing in the organization as well as consideration of the implementation, mitigation, and budgetary constraints of the organization. The goal of this paper was to improve the information security control analysis method by proposing a formalized approach, i.e., fuzzy Analytical Hierarchy Process (AHP). This approach was used to prioritize and select the most relevant set of information security controls to satisfy the information security requirements of an organization. We argue that the prioritization of the information security controls using fuzzy AHP leads to an efficient and cost-effective assessment and evaluation of information security controls for an organization in order to select the most appropriate ones. The proposed formalized approach and prioritization processes are based on International Organization for Standardization and the International Electrotechnical Commission (ISO/IEC) 27001:2013. But in practice, organizations may apply this approach to any information security baseline manual. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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Review

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24 pages, 3309 KiB

Open AccessReview

Integration of Novel Sensors and Machine Learning for Predictive Maintenance in Medium Voltage Switchgear to Enable the Energy and Mobility Revolutions

by Martin W. Hoffmann, Stephan Wildermuth, Ralf Gitzel, Aydin Boyaci, Jörg Gebhardt, Holger Kaul, Ido Amihai, Bodo Forg, Michael Suriyah, Thomas Leibfried, Volker Stich, Jan Hicking, Martin Bremer, Lars Kaminski, Daniel Beverungen, Philipp zur Heiden and Tanja Tornede

Sensors 2020, 20(7), 2099; https://doi.org/10.3390/s20072099 - 8 Apr 2020

Cited by 90 | Viewed by 12649

Abstract

The development of renewable energies and smart mobility has profoundly impacted the future of the distribution grid. An increasing bidirectional energy flow stresses the assets of the distribution grid, especially medium voltage switchgear. This calls for improved maintenance strategies to prevent critical failures. Predictive maintenance, a maintenance strategy relying on current condition data of assets, serves as a guideline. Novel sensors covering thermal, mechanical, and partial discharge aspects of switchgear, enable continuous condition monitoring of some of the most critical assets of the distribution grid. Combined with machine learning algorithms, the demands put on the distribution grid by the energy and mobility revolutions can be handled. In this paper, we review the current state-of-the-art of all aspects of condition monitoring for medium voltage switchgear. Furthermore, we present an approach to develop a predictive maintenance system based on novel sensors and machine learning. We show how the existing medium voltage grid infrastructure can adapt these new needs on an economic scale. Full article

(This article belongs to the Special Issue Sensors for Societal Automation)

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