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Special Issue "Sense and Respond: Industrial Applications of Smart Sensors in Cyber-Physical Systems"

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

Deadline for manuscript submissions: 31 January 2020.

Special Issue Editors

Guest Editor
Prof. Dr. Javier Villalba Diez

Hochschule Heilbronn, Campus Schwäbisch Hall, 74081 Heilbronn, Germany
Universidad Politécnica de Madrid, Escuela Técnica Superior de Ingenieros Informáticos, Artificial Intelligence Department, 28660 Boadilla del Monte, Madrid, Spain
Website | E-Mail
Interests: artificial intelligence, deep learning, cyber-physical systems, business intelligence, strategic organizational design
Guest Editor
Prof. Dr. Joaquín Ordieres-Meré

Department of Industrial Engineering, Universidad Politécnca de Madrid, José Gituérrez Abascal 2, 28006 Madrid, Spain
Website | E-Mail
Interests: Big Analytics; IIoT; Smart Sensors; Digital Transformation of Industry; artificial intelligence; Machine Learning; Distributed Computing

Special Issue Information

Dear Colleagues,

Over the past century, the manufacturing industry has undergone a number of paradigm shifts: from the Ford assembly line (1900s) and its focus on efficiency to the Toyota production system (1960s) and its focus on effectiveness and JIDOKA, from flexible manufacturing (1980s) to reconfigurable manufacturing (1990s) (both following the trend of mass customization), and from agent-based manufacturing (2000s) to cloud manufacturing (2010s) (both deploying the value stream complexity into the material and information flow, respectively).

The next natural evolutionary step is to provide value by creating industrial cyber-physical assents with human-like intelligence. This will only be possible by further integrating strategic smart sensors technology into the manufacturing cyber-physical value creating processes in which industrial equipment is monitored and controlled for analyzing compression, temperature, moisture, vibrations, and performance. For instance, in the new wave of the ‘Industrial Internet of Things’ (IIoT), smart sensors will enable the development of new applications by interconnecting software, machines, and humans throughout the manufacturing process, thus enabling suppliers and manufacturers to rapidly respond to changing standards.

This Special Issue “Sense and Respond” aims to cover recent developments in the field of industrial applications, especially smart sensors technologies that increase the productivity, quality, reliability, and safety of industrial cyber-physical value-creating processes. In particular, submitted papers should clearly show novel contributions and innovative applications covering- but not limited to any of the following topics:

  • Sensor networks;
  • IIoT related to production, safety, and/or health in the workplace, including pollution;
  • Industrial applications of smart sensors to (although not restricted to) cloud computing, mobile technologies, 3D printing, advanced robotics, big data, Internet of Things, RFID technology, and cognitive computing, that enable better value stream performance;
  • Applications of smart sensors that optimize the energy consumption of value creating processes and reduce the CO2 manufacturing footprint, i.e., smart grids;
  • IIoT and integration between workers and process automation to produce a more comprehensive perspective;
  • Industrial applications on cloud computing, artificial intelligence, and machine learning that enable a better value stream performance;
  • Applications of sensor networks to cyber-physical production systems;
  • Trust and accountability through DLT (distributed ledger technology) in industrial applications.

Prof. Dr. Javier Villalba Diez
Prof. Dr. Joaquín Ordieres-Meré
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 papers will be 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 1800 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

  • Smart sensors in industrial applications
  • Industrial Internet of Things
  • Cloud computing
  • 3D printing
  • Advanced robotics
  • Big data in industrial applications
  • RFID technology
  • Cognitive computing
  • Smart grid
  • Artificial intelligence in industrial applications
  • Cyber-physical production systems
  • Distributed ledger technology in industrial applications.

Published Papers (3 papers)

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Research

Open AccessArticle
Accuracy Analysis in Sensor Networks for Asynchronous Positioning Methods
Sensors 2019, 19(13), 3024; https://doi.org/10.3390/s19133024
Received: 6 June 2019 / Revised: 8 July 2019 / Accepted: 8 July 2019 / Published: 9 July 2019
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Abstract
The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based [...] Read more.
The accuracy requirements for sensor network positioning have grown over the last few years due to the high precision demanded in activities related with vehicles and robots. Such systems involve a wide range of specifications which must be met through positioning devices based on time measurement. These systems have been traditionally designed with the synchronization of their sensors in order to compute the position estimation. However, this synchronization introduces an error in the time determination which can be avoided through the centralization of the measurements in a single clock in a coordinate sensor. This can be found in typical architectures such as Asynchronous Time Difference of Arrival (A-TDOA) and Difference-Time Difference of Arrival (D-TDOA) systems. In this paper, a study of the suitability of these new systems based on a Cramér-Rao Lower Bound (CRLB) evaluation was performed for the first time under different 3D real environments for multiple sensor locations. The analysis was carried out through a new heteroscedastic noise variance modelling with a distance-dependent Log-normal path loss propagation model. Results showed that A-TDOA provided less uncertainty in the root mean square error (RMSE) in the positioning, while D-TDOA reduced the standard deviation and increased stability all over the domain. Full article
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Open AccessArticle
3D Tdoa Problem Solution with Four Receiving Nodes
Sensors 2019, 19(13), 2892; https://doi.org/10.3390/s19132892
Received: 8 May 2019 / Revised: 26 June 2019 / Accepted: 27 June 2019 / Published: 29 June 2019
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Abstract
Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two [...] Read more.
Time difference of arrival (TDOA) positioning methods have experienced growing importance over the last few years due to their multiple applications in local positioning systems (LPSs). While five sensors are needed to determine an unequivocal three-dimensional position, systems with four nodes present two different solutions that cannot be discarded according to mathematical standards. In this paper, a new methodology to solve the 3D TDOA problems in a sensor network with four beacons is proposed. A confidence interval, which is defined in this paper as a sphere, is defined to use positioning algorithms with four different nodes. It is proven that the separation between solutions in the four-beacon TDOA problem allows the transformation of the problem into an analogous one in which more receivers are implied due to the geometric properties of the intersection of hyperboloids. The achievement of the distance between solutions needs the application of genetic algorithms in order to find an optimized sensor distribution. Results show that positioning algorithms can be used 96.7% of the time with total security in cases where vehicles travel at less than 25 m/s. Full article
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Open AccessArticle
Characterization of Industry 4.0 Lean Management Problem-Solving Behavioral Patterns Using EEG Sensors and Deep Learning
Sensors 2019, 19(13), 2841; https://doi.org/10.3390/s19132841
Received: 20 May 2019 / Revised: 9 June 2019 / Accepted: 21 June 2019 / Published: 26 June 2019
PDF Full-text (8685 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, [...] Read more.
Industry 4.0 leaders solve problems all of the time. Successful problem-solving behavioral pattern choice determines organizational and personal success, therefore a proper understanding of the problem-solving-related neurological dynamics is sure to help increase business performance. The purpose of this paper is two-fold: first, to discover relevant neurological characteristics of problem-solving behavioral patterns, and second, to conduct a characterization of two problem-solving behavioral patterns with the aid of deep-learning architectures. This is done by combining electroencephalographic non-invasive sensors that capture process owners’ brain activity signals and a deep-learning soft sensor that performs an accurate characterization of such signals with an accuracy rate of over 99% in the presented case-study dataset. As a result, the deep-learning characterization of lean management (LM) problem-solving behavioral patterns is expected to help Industry 4.0 leaders in their choice of adequate manufacturing systems and their related problem-solving methods in their future pursuit of strategic organizational goals. Full article
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