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Special Issue "Sensor Applications in Industrial Automation"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editors

Prof. Dr. Paulo Pedreiras
Guest Editor
Electronics, Telecommunications and Informatics Department, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: distributed real-time systems; industrial communications; real-time scheduling; real-time medium access control; dynamic quality-of-service management; industrial internet of things; cyberphysical systems
Prof. Dr. João Paulo Barraca
Website SciProfiles
Guest Editor
Telecommunications Institute - Aveiro, and Electronics, Telecommunications and Informatics Department, University of Aveiro, 3810-193 Aveiro, Portugal
Interests: Internet of Things; software-defined networks; services and network security
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

In the era of the Fourth Industrial Revolution, sensing plays a crucial role in Industrial Automation by interfacing the digital and physical worlds. Sensory systems allow reading a plethora of relevant physical parameters and variables that are then used, at the digital upper layers, for multiple objectives, including control and monitoring of physical processes, maintenance and failure prediction, and resource management and process optimization.

Developing effective sensing systems in the dawn of the Fourth Industrial Revolution is especially challenging. Sensing devices must meet classic and inherent metrological requirements, being able to carry out accurate measurements, sometimes in harsh environments and conditions, while being subject to strict size, weight, and power constraints. Once acquired, data must be transmitted effectively, which implies that the networking infrastructures have to satisfy heterogeneous and often conflicting requirements, among which predictability, timeliness, reliability, security, bandwidth and energy efficiency, and integration and heterogeneity play a fundamental role. At the end of the chain, it is necessary to process, explore, and store the data effectively, and thus, emerging technologies and concepts such as Big Data and Machine and Deep Learning are of extreme importance. Finally, architectural aspects, such as how to distribute sensor data processing over the different layers, global resource management schemes and policies, and methods for assuring end-to-end QoS are also essential to allow the deployment of sensing systems able to support the requirements of emerging industrial automation applications. Orthogonal to these aspects is the security of all components and interactions, as failure to detect and block security compromises may lead to extensive losses, and even human injury. This further introduces the need for controls that keep components operating in a predictable manner.

This Special Issue aims to highlight the latest research results and advances on technologies for sensor applications in Industrial Automation; therefore, we welcome the submission of original papers presenting significant advances with respect to the state of the art, featuring a solid theoretical development and practical relevance. Topics of interest falling under the scope of Smart Factories and Industry 4.0 include but are not limited to:

  • Big Data, sensor data fusion data analytics;
  • Design principles and practices for 5G integrated factories;
  • Energy harvesting and power management for industrial automation;
  • IA, Machine Learning, and Deep Learning;
  • Industrial sensors, sensor virtualization, and Digital Twins;
  • Integration and holistic management architectures and frameworks;
  • Intrusion detection/prevention/prediction techniques and system integrity;
  • Latency restricted IIoT applications with 5G;
  • Localization and tracking for indoor and outdoor industrial applications;
  • Machine-to-Machine architectures and protocols;
  • Multiconnectivity through 5G;
  • Network slicing challenges and solutions;
  • Novel sensing systems, architectures, and frameworks;
  • Performance evaluation of industrial automation systems, platforms, and protocols;
  • Real-time and networked embedded systems;
  • Secure integration of IoT/IIoT and Cloud, Fog, and Edge Computing;
  • Security controls and mechanisms;
  • Software-defined factories;
  • Very-high-density 5G IIoT networks;
  • Web services and service-oriented architectures;
  • Wireless sensor networks and protocols for IoT/IIoT;
  • Case studies of IoT/IIoT-based SCADA applications;
  • Case studies.

Prof. Dr. Paulo Pedreiras
Prof. Dr. João Paulo Barraca
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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 2000 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.


  • 5G and beyond
  • Big data, sensor data fusion, data analytics
  • Configuration and management
  • Connected factories
  • Fault tolerance
  • Fog and Edge Computing
  • High-density networking
  • Industrial wireless sensor networks
  • Integration and Interoperability
  • M2M communication
  • Machine Learning, Deep Learning
  • Networked Embedded Systems
  • Real-time communication and applications
  • Safety and Security
  • Service Oriented Architectures
  • Web-based communication and applications
  • Case studies

Published Papers (1 paper)

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Open AccessArticle
Denoising Autoencoders and LSTM-Based Artificial Neural Networks Data Processing for Its Application to Internal Model Control in Industrial Environments—The Wastewater Treatment Plant Control Case
Sensors 2020, 20(13), 3743; - 04 Jul 2020
The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This [...] Read more.
The evolution of industry towards the Industry 4.0 paradigm has become a reality where different data-driven methods are adopted to support industrial processes. One of them corresponds to Artificial Neural Networks (ANNs), which are able to model highly complex and non-linear processes. This motivates their adoption as part of new data-driven based control strategies. The ANN-based Internal Model Controller (ANN-based IMC) is an example which takes advantage of the ANNs characteristics by modelling the direct and inverse relationships of the process under control with them. This approach has been implemented in Wastewater Treatment Plants (WWTP), where results show a significant improvement on control performance metrics with respect to (w.r.t.) the WWTP default control strategy. However, this structure is very sensible to non-desired effects in the measurements—when a real scenario showing noise-corrupted data is considered, the control performance drops. To solve this, a new ANN-based IMC approach is designed with a two-fold objective, improve the control performance and denoise the noise-corrupted measurements to reduce the performance degradation. Results show that the proposed structure improves the control metrics, (the Integrated Absolute Error (IAE) and the Integrated Squared Error (ISE)), around a 21.25% and a 54.64%, respectively. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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