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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: closed (30 June 2022) | Viewed by 39153

Special Issue Editors


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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; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
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, Collections and Topics 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

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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

  • 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

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

Published Papers (11 papers)

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19 pages, 2431 KiB  
Article
Extending MQTT with Real-Time Communication Services Based on SDN
by Ehsan Shahri, Paulo Pedreiras and Luis Almeida
Sensors 2022, 22(9), 3162; https://doi.org/10.3390/s22093162 - 20 Apr 2022
Cited by 16 | Viewed by 4807
Abstract
MQTT is one of the most popular application-layer protocols used in the scope of the Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT), given its suitability for resource-constrained embedded systems. However, MQTT Quality-of-Service policies do not support timeliness requirements, which is common in IIoT. The literature [...] Read more.
MQTT is one of the most popular application-layer protocols used in the scope of the Internet-of-Things (IoT) and Industrial-Internet-of-Things (IIoT), given its suitability for resource-constrained embedded systems. However, MQTT Quality-of-Service policies do not support timeliness requirements, which is common in IIoT. The literature reports several research works that address this limitation, but they are limited in scope (e.g., improvements in the broker’s internal operation, control of the publisher’s data rate, and path optimizations). Conversely, this paper presents a comprehensive architectural approach, proposing a set of extensions to the MQTT protocol that allow applications to explicitly specify real-time requirements and instantiate corresponding network reservations to enforce the desired temporal behavior. Such reservations are enforced via Software Defined Networking, specifically the OpenFlow protocol, but other protocols that allow bandwidth reservations, e.g., TSN, can also be used. This paper presents the proposed system architecture together with extensive emulation and implementation results that validate the feasibility of the approach, showing that time-sensitive MQTT traffic can be effectively segregated and prioritized to meet application-defined real-time requirements. Using several combinations of network topologies and load levels and comparing to the absence of the proposed real-time mechanisms, both average and worst-case latencies of the time-sensitive traffic decreased to approximately half, while for the normal traffic, they increased by approximately 10%. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Figure 1
<p>High-level RT-MQTT system architecture.</p>
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<p>Structure of real-time attributes specification in RT-MQTT.</p>
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<p>RT-NM operation flow diagram.</p>
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<p>Network configuration process.</p>
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<p>Message set up sequence diagram.</p>
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<p>Network topologies used in the experiments.</p>
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<p>Latency measurement (L) in the RT-MQTT experiments.</p>
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<p>Latency of time-sensitive vs. normal MQTT flows with and without real-time extensions for the Simple topology and load levels A, B, and C.</p>
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<p>Latency of time-sensitive vs. normal MQTT flows with and without real-time extensions for the Medium topology and load levels A, B, and C.</p>
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<p>Latency of time-sensitive vs. normal MQTT flows with and without real-time extensions for the Hard topology and load levels A, B, and C.</p>
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<p>Experimental set-up architecture.</p>
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<p>View of the complete experimental setup.</p>
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<p>Latency of time-sensitive vs. normal MQTT flows with and without real-time extensions for the Simple topology physical implementation with load levels A, B, and C.</p>
Full article ">
19 pages, 3852 KiB  
Article
Optimal Relay Network for Aerial Remote Inspections
by Luis Ramos Pinto and Luis Almeida
Sensors 2022, 22(4), 1391; https://doi.org/10.3390/s22041391 - 11 Feb 2022
Cited by 1 | Viewed by 1852
Abstract
Unmanned aerial vehicles (UAVs), in particular multirotors, are becoming the de facto tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult-to-access structures, such as chimneys, deposits, and towers. However, the [...] Read more.
Unmanned aerial vehicles (UAVs), in particular multirotors, are becoming the de facto tool for aerial sensing and remote inspection. In large industrial facilities, a UAV can transmit an online video stream to inspect difficult-to-access structures, such as chimneys, deposits, and towers. However, the communication range is limited, constraining the UAV operation range. This limitation can be overcome with relaying UAVs placed between the source UAV and the control station, creating a line of communication links. In this work, we assume the use of a digital data packet network technology, namely WiFi, and tackle the problem of defining the exact placement for the relaying UAVs that creates an end-to-end channel with maximal delivery of data packets. We consider asymmetric communication links and we show an increase as large as 15% in end-to-end packet delivery ratio when compared to an equidistant placement. We also discuss the deployment of such a network and propose a fully distributed method that converges to the global optimal relay positions taking, on average, 1.4 times the time taken by a centralized method. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Figure 1
<p>Single-source aerial stream to a ground sink using UAV relays to transmit data.</p>
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<p>Multi-hop line network model. The PDR of link <span class="html-italic">i</span>, between nodes <span class="html-italic">i</span> and <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>, is <math display="inline"><semantics> <msub> <mi>P</mi> <mi>i</mi> </msub> </semantics></math> with parameters <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>i</mi> </msub> <mo>,</mo> <msub> <mi>α</mi> <mi>i</mi> </msub> </mrow> </semantics></math>, and its length is <math display="inline"><semantics> <msub> <mi>d</mi> <mi>i</mi> </msub> </semantics></math>.</p>
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<p>PDR of link 1 (red), link 2 (green), and their product, i.e., network (black), as a function of the length of the first link <math display="inline"><semantics> <msub> <mi>d</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>L</mi> <mo>=</mo> <mn>140</mn> </mrow> </semantics></math>.</p>
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<p>PDR of link 1, link 2, and link 3 as a function of their length.</p>
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<p><math display="inline"><semantics> <msub> <mi>P</mi> <mi mathvariant="monospace">net</mi> </msub> </semantics></math> (Network PDR) as a function of the length of the three links (<math display="inline"><semantics> <msub> <mi>d</mi> <mn>1</mn> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>d</mi> <mn>2</mn> </msub> </semantics></math> are represented in the horizontal plane and <math display="inline"><semantics> <msub> <mi>d</mi> <mn>3</mn> </msub> </semantics></math> is a parameter shown in the legend). The maximum PDR is shown as a black dot, belonging to the black curve corresponding to the value of <math display="inline"><semantics> <msub> <mi>d</mi> <mn>3</mn> </msub> </semantics></math> marked with asterisks in the legend.</p>
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<p>PDR models of six links as a function of link length <math display="inline"><semantics> <msub> <mi>d</mi> <mi>i</mi> </msub> </semantics></math>, where link <span class="html-italic">i</span> connects nodes <span class="html-italic">i</span> and <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>∀</mo> <mi>i</mi> <mo>∈</mo> <mo>[</mo> <mn>1</mn> <mo>,</mo> <mn>6</mn> <mo>]</mo> </mrow> </semantics></math>.</p>
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<p>The optimal relay positions (solid lines) are different than the corresponding equidistant positions (dashed lines). Their absolute differences increase (nonlinearly) with network length <span class="html-italic">L</span>.</p>
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<p>The relative positions of the relays as fractions of the total network length change with the network length itself.</p>
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<p>PDR <math display="inline"><semantics> <msub> <mi>P</mi> <mi mathvariant="monospace">net</mi> </msub> </semantics></math> improves significantly when the optimal placement is used instead of equidistant positions, especially for larger network lengths.</p>
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<p>PDR <math display="inline"><semantics> <msub> <mi>P</mi> <mi mathvariant="monospace">net</mi> </msub> </semantics></math> relative gain (<math display="inline"><semantics> <mi mathvariant="sans-serif">Φ</mi> </semantics></math>) using the optimal placement with respect to equidistant positioning.</p>
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<p>Pool of link PDR models <math display="inline"><semantics> <mrow> <mo>(</mo> <mi>R</mi> <mo>,</mo> <mi>α</mi> <mo>)</mo> </mrow> </semantics></math> used for the general performance characterization (<math display="inline"><semantics> <mrow> <mi>R</mi> <mo>∈</mo> <mo>[</mo> <mn>80</mn> <mo>,</mo> <mn>180</mn> <mo>]</mo> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>α</mi> <mo>∈</mo> <mo>[</mo> <mn>2.1</mn> <mo>,</mo> <mn>4.2</mn> <mo>]</mo> </mrow> </semantics></math>).</p>
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<p>Average network PDR gain <math display="inline"><semantics> <mi mathvariant="sans-serif">Φ</mi> </semantics></math> (optimal relative to equidistant relay placement) with random link models, as a function of the number of links and network length <span class="html-italic">L</span>.</p>
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<p>Nodes converging to their optimal position. Triangles represent relay positions every step of the distributed approach. Solid lines show the evolution of a centralized approach. Iterations with <span class="html-italic">T</span> = 1 s (<b>top</b>) and <span class="html-italic">T</span> = 5 s (<b>bottom</b>); note the different horizontal scales.</p>
Full article ">Figure 14
<p>Distribution of the ratio distributed over centralized convergence time for different values of <span class="html-italic">T</span>. (<b>Left</b>): <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math> s; (<b>Right</b>): <math display="inline"><semantics> <mrow> <mi>T</mi> <mo>=</mo> <mn>5</mn> </mrow> </semantics></math> s.</p>
Full article ">
32 pages, 868 KiB  
Article
Reliability Analysis of the Proactive Transmission of Replicated Frames Mechanism over Time-Sensitive Networking
by Inés Álvarez, Manuel Barranco and Julián Proenza
Sensors 2021, 21(24), 8427; https://doi.org/10.3390/s21248427 - 17 Dec 2021
Cited by 3 | Viewed by 2231
Abstract
The Time-Sensitive Networking (TSN) Task Group has standardised different mechanisms to provide Ethernet with hard real-time guarantees and reliability in layer 2 of the network architecture. Specifically, TSN proposes using space redundancy to increase the reliability of Ethernet networks, but using space redundancy [...] Read more.
The Time-Sensitive Networking (TSN) Task Group has standardised different mechanisms to provide Ethernet with hard real-time guarantees and reliability in layer 2 of the network architecture. Specifically, TSN proposes using space redundancy to increase the reliability of Ethernet networks, but using space redundancy to tolerate temporary faults is not a cost-effective solution. For this reason, we propose to use time redundancy to tolerate temporary faults in the links of TSN-based networks. Specifically, in previous works we proposed the Proactive Transmission of Replicated Frames (PTRF) mechanism to tolerate temporary faults in the links. Now, in this work we present a series of models of TSN and PTRF developed using PRISM, a probabilistic model checker that can be used to evaluate the reliability of systems. After that, we carry out a parametric sensitivity analysis of the reliability achievable by TSN and PTRF and we show that we can increase the reliability of TSN-based networks using PTRF to tolerate temporary faults in the links of TSN networks. This is the first work that presents a quantitative analysis of the reliability of TSN networks. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Figure 1

Figure 1
<p>Example of a TSN-based network with three end-systems and two bridges. Two end-systems act as talker and listener and the third one acts as listener of two streams.</p>
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<p>Example of a communication cycle in an output port of a device. The communication cycle is divided into a time-triggered, an event-triggered and a best-effort window.</p>
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<p>Behaviour of the three approaches of PTRF in the presence of temporary faults in the links. Reproduced as in [<a href="#B11-sensors-21-08427" class="html-bibr">11</a>]. (<b>a</b>) Behaviour of the approach A in the presence of temporary faults in the links. (<b>b</b>) Behaviour of the approach B in the presence of temporary faults in the links. (<b>c</b>) Behaviour of the approach C in the presence of temporary faults in the links.</p>
Full article ">Figure 4
<p>Basic architecture of a TSN bridge with the PTRF mechanism. The additional components PTRF includes are highlighted in blue. Solid lines represent the path frames follow inside the bridge, whereas discontinuous lines represent management or configuration interactions. Reproduced as in [<a href="#B11-sensors-21-08427" class="html-bibr">11</a>].</p>
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<p>Inclusion hierarchy diagram that depicts the presented failure modes. Figure based on Figure 2.9 in [<a href="#B32-sensors-21-08427" class="html-bibr">32</a>].</p>
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<p>State diagram for the model of a link.</p>
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<p>Modules in the general model. The module system evaluation decides whether the system has failed, the module path models the transmission of frames, and the modules phase and period manage the pass of time in the model.</p>
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<p>Blocks that constitute the path module.</p>
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<p>Execution of the phase and period modules to dictate the pass of time in the models.</p>
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<p>Reliability achievable when varying the BER from <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>12</mn> </mrow> </msup> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>. TSN is represented in gray, approach A in orange and approach B in green. The <span class="html-italic">x</span> axis represents the BER values, while the <span class="html-italic">y</span> axis represents the reliability.</p>
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<p>Zoom in the reliability achievable by TSN, approach A and approach B for BERs from <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>8</mn> </mrow> </msup> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>4</mn> </mrow> </msup> </mrow> </semantics></math>. TSN is represented in grey, approach A in orange and approach B in green. The <span class="html-italic">x</span> axis represents the BER values, while the <span class="html-italic">y</span> axis represents the reliability.</p>
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<p>Reliability when varying the frame size. The left side of the figure shows the reliability for a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> </mrow> </semantics></math>, while the right shows the reliability for a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics></math>. In both cases, TSN is represented in grey, approach A in orange and approach B in green. The <span class="html-italic">x</span> axis represents the frame sizes in bytes, while the <span class="html-italic">y</span> axis represents the reliability.</p>
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<p>Reliability achievable when varying the number of replicas in a network with a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> </mrow> </semantics></math>. TSN is represented in grey, approach A in orange and approach B in green. The <span class="html-italic">x</span> axis represents the number of replicas, while the <span class="html-italic">y</span> axis represents the reliability. (<b>a</b>) Reliability achievable by TSN, approach A and approach B when varying the number of replicas. In TSN the number of replicas is always 1. (<b>b</b>) Zoom in the reliability achievable by approach A and approach B when varying the number of replicas.</p>
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<p>Reliability achievable when varying the number of replicas in a network with a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics></math>. Approach A is represented in orange and approach B in green. The <span class="html-italic">x</span> axis represents the number of replicas, while the <span class="html-italic">y</span> axis represents the reliability.</p>
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<p>Reliability achievable when varying the number of bridges in a network with a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </msup> </mrow> </semantics></math>. TSN is represented in grey, approach A in orange and approach B in green. The <span class="html-italic">x</span> axis represents the number of bridges, while the <span class="html-italic">y</span> axis represents the reliability. (<b>a</b>) Reliability achievable by TSN, approach A and approach B when varying the number of bridges. (<b>b</b>) Zoom in the reliability achievable by approach A and approach B when varying the number of bridges.</p>
Full article ">Figure 16
<p>Reliability achievable when varying the number of bridges in a network with a BER of <math display="inline"><semantics> <mrow> <mn>1</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>6</mn> </mrow> </msup> </mrow> </semantics></math>. Approach A is represented in orange and approach B in green. The <span class="html-italic">x</span> axis represents the number of bridges, while the <span class="html-italic">y</span> axis represents the reliability.</p>
Full article ">
16 pages, 2101 KiB  
Article
Pervasive Digital Twin for PI-Containers: A New Packing Problem
by Patrick Charpentier, Frédéric Chaxel, Nicolas Krommenacker, Vincent Bombardier and Fabian Seguel
Sensors 2021, 21(23), 7999; https://doi.org/10.3390/s21237999 - 30 Nov 2021
Cited by 4 | Viewed by 2175
Abstract
The idea defended in this paper consists in finding, at any time and everywhere, the arrangement of containers within a composite container. The digital image of the real arrangement obtained defines its digital twin. This image evolves at the same time as its [...] Read more.
The idea defended in this paper consists in finding, at any time and everywhere, the arrangement of containers within a composite container. The digital image of the real arrangement obtained defines its digital twin. This image evolves at the same time as its real twin. It can be used throughout the logistics chain during loading/unloading phases in hubs, to check the completeness of a load, to find the particular position of a container, etc. This digital twin is obtained through the collection of neighborhood information from the sensor nodes embedded on each container. This embedded solution allows accessibility to this information everywhere. This proximity information and the instrumentation of the containers define new types of constraints and a new version of a packing problem. We propose here a model integrating them. This model is implemented and tested on different test cases, and numerical results are provided. These show that, under certain conditions that will be presented, it is possible to obtain the digital twin of the real arrangement. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Figure 1
<p>Life cycle of real arrangement and its digital twin.</p>
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<p>The possible space occupations of a parallelepiped in the 3D space.</p>
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<p>Impact of instrumentation on the combinatory of the problem due to the position of the node on the container.</p>
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<p>Links between FLB corner and node position.</p>
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<p>Meller’s scenario with containers and composite container.</p>
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<p>Arrangement of 18 containers with identical dimensions.</p>
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<p>Enumeration of solutions according to the coverage radius (without uncertainty).</p>
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<p>Influence of the uncertainty on the number of solutions according coverage radius (Meller’s scenario).</p>
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<p>Histogram of the solutions number per range—Meller’s scenario.</p>
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<p>Histogram of the solutions number per range—18 containers scenario.</p>
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19 pages, 975 KiB  
Article
Unfairness of Random Access with Collision Avoidance in Industrial Internet of Things Networks
by Marek Miśkowicz
Sensors 2021, 21(21), 7135; https://doi.org/10.3390/s21217135 - 27 Oct 2021
Cited by 6 | Viewed by 2408
Abstract
This paper is focused on the analysis of unfairness of random media access in Local Operating Networks (LON), which is one of the commercial platforms of the Industrial Internet of Things (IIoT). The unfairness in accessing the LON channel is introduced by a [...] Read more.
This paper is focused on the analysis of unfairness of random media access in Local Operating Networks (LON), which is one of the commercial platforms of the Industrial Internet of Things (IIoT). The unfairness in accessing the LON channel is introduced by a collision avoidance mechanism in the predictive p-persistent CSMA protocol adopted at the media access control layer. The study on the bandwidth share in predictive p-persistent CSMA calls for the analysis of multiple memoryless backoff. In this paper, it is shown that the channel access in LON systems is unfair in the short term for medium traffic load conditions, and in the long term for heavy loaded networks. Furthermore, it is explained that the average bandwidth allocated to a particular node is determined implicitly by the load scenario, while an actual node bandwidth fluctuates in time according to stochastic dynamics of the predictive p-persistent CSMA. Next, it is formally proven that the average bandwidth available to a node is a linear function of its backoff state and does not depend on backoff states of the other stations. Finally, it is demonstrated that possibly unfair bandwidth share in LON networks determined implicitly by load scenario is stable because, with lowering a fraction of actual network bandwidth accessible by a given station, the probability to decrease it in the future also drops. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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<p>Backlog counting algorithm for transmitting nodes (<b>left</b> diagram), and for receiving nodes (<b>right</b> diagram) if the nodes are not equipped with collision detection.</p>
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<p>Backlog counting algorithm for transmitting nodes (<b>left</b> diagram) and receiving nodes (<b>right</b> diagram) if they are able to detection collisions.</p>
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<p>Plots of <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>c</mi> <mi>c</mi> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mo stretchy="false">(</mo> <mi>n</mi> <mo>−</mo> <mi>m</mi> <mo>,</mo> <mi>m</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>c</mi> <mi>c</mi> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mo stretchy="false">(</mo> <mi>n</mi> <mo>−</mo> <mi>m</mi> <mo>,</mo> <mi>m</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> vs. number of nodes (<span class="html-italic">m</span>) that occupy backlog state <span class="html-italic">k</span><sub>2</sub> and <math display="inline"><semantics> <mrow> <msubsup> <mi>p</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>c</mi> <mi>c</mi> <mo stretchy="false">(</mo> <mn>1</mn> <mo stretchy="false">)</mo> </mrow> <mrow> <mo stretchy="false">(</mo> <mn>2</mn> <mo stretchy="false">)</mo> </mrow> </msubsup> <mo stretchy="false">(</mo> <mn>0</mn> <mo>,</mo> <mi>n</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> according to (1), (2), (3), and <math display="inline"><semantics> <mrow> <msub> <mi>p</mi> <mrow> <mi>s</mi> <mi>u</mi> <mi>c</mi> <mi>c</mi> </mrow> </msub> <mo stretchy="false">(</mo> <mi>n</mi> <mo>−</mo> <mi>m</mi> <mo>,</mo> <mi>m</mi> <mo stretchy="false">)</mo> </mrow> </semantics></math> according to (9) for <span class="html-italic">n</span> = 5, <span class="html-italic">k</span><sub>1</sub> = 1, and <span class="html-italic">k</span><sub>2</sub> = 3.</p>
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28 pages, 22542 KiB  
Article
Enhancing SDN WISE with Slicing Over TSCH
by Federico Orozco-Santos, Víctor Sempere-Payá, Teresa Albero-Albero and Javier Silvestre-Blanes
Sensors 2021, 21(4), 1075; https://doi.org/10.3390/s21041075 - 4 Feb 2021
Cited by 16 | Viewed by 3505
Abstract
IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic [...] Read more.
IWSNs (Industrial Wireless Sensor Networks) have become the next step in the evolution of WSN (Wireless Sensor Networks) due to the nature and demands of modern industry. With this type of network, flexible and scalable architectures can be created that simultaneously support traffic sources with different characteristics. Due to the great diversity of application scenarios, there is a need to implement additional capabilities that can guarantee an adequate level of reliability and that can adapt to the dynamic behavior of the applications in use. The use of SDNs (Software Defined Networks) extends the possibilities of control over the network and enables its deployment at an industrial level. The signaling traffic exchanged between nodes and controller is heavy and must occupy the same channel as the data traffic. This difficulty can be overcome with the segmentation of the traffic into flows, and correct scheduling at the MAC (Medium Access Control) level, known as slices. This article proposes the integration in the SDN controller of a traffic manager, a routing process in charge of assigning different routes according to the different flows, as well as the introduction of the Time Slotted Channel Hopping (TSCH) Scheduler. In addition, the TSCH (Time Slotted Channel Hopping) is incorporated in the SDN-WISE framework (Software Defined Networking solution for Wireless Sensor Networks), and this protocol has been modified to send the TSCH schedule. These elements are jointly responsible for scheduling and segmenting the traffic that will be sent to the nodes through a single packet from the controller and its performance has been evaluated through simulation and a testbed. The results obtained show how flexibility, adaptability, and determinism increase thanks to the joint use of the routing process and the TSCH Scheduler, which makes it possible to create a slicing by flows, which have different quality of service requirements. This in turn helps guarantee their QoS characteristics, increase the PDR (Packet Delivery Ratio) for the flow with the highest priority, maintain the DMR (Deadline Miss Ratio), and increase the network lifetime. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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<p>(<b>a</b>) Software Defined Network (SDN) architecture, (<b>b</b>) beacon packet, (<b>c</b>) report packet.</p>
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<p>Structure of the original OpenPath packet. (<b>a</b>) Generic example, (<b>b</b>) example with three-node Route and one Flow Rule.</p>
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<p>Flow between nodes 10 and 1. (<b>a</b>) Simple Dijkstra, based on Received Signal Strength Indicator (RSSI), (<b>b</b>) accumulated Dijkstra, balanced load.</p>
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<p>Scheduling flows with different deadlines.</p>
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<p>Structure of the OpenPathTSCH packet. Generic proposal.</p>
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<p>Topology of 10 nodes. Time Slotted Channel Hopping (TSCH) Scheduler of a 5-node Path with NR = 2.</p>
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<p>OpenPathTSCH packet structure for a 5-node route.</p>
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<p>Software Defined Networking solution for Wireless Sensor Networks (SDN-WISE) TSCH convergence time with TSCH synchronization; (<b>a</b>) Convergence time; (<b>b</b>) Trend Convergence time</p>
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<p>Control traffic generated by Routing Protocol for Low-Power and Lossy Networks (RPL) vs. SDN-WISE.</p>
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<p>Response and change of scheduling time.</p>
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<p>(<b>a</b>) Packet inter-arrival time for all flows, (<b>b</b>) Number of packets by flow.</p>
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<p>Distribution of flows over the nodes; (<b>a</b>) SDN WISE TSCH; (<b>b</b>) single path for all nodes; (<b>c</b>) network lifetime for both.</p>
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<p>Path change after node 8 failure; (<b>a</b>) Packet inter arrival time; (<b>b</b>) Packet delivery ratio</p>
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<p>Comparison between SDN WISE TSCH and Distributed Scheduling.</p>
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<p>Effect of multiple transmission nodes in test topology; (<b>a</b>) Packet inter arrival time (node 10); (<b>b</b>) Packet inter arrival time (node 9); (<b>c</b>) Packet delivery ratio</p>
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<p>Testbed for 10 nodes topology.</p>
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<p>Performance with simulated failure in testbed; (<b>a</b>) Packet inter arrival time; (<b>b</b>) Packet delivery ratio; (<b>c</b>) End to End Delay</p>
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29 pages, 945 KiB  
Article
Design and Experimental Evaluation of the Proactive Transmission of Replicated Frames Mechanism over Time-Sensitive Networking
by Inés Álvarez, Ignasi Furió, Julián Proenza and Manuel Barranco
Sensors 2021, 21(3), 756; https://doi.org/10.3390/s21030756 - 23 Jan 2021
Cited by 12 | Viewed by 2561
Abstract
In recent years the Time-Sensitive Networking (TSN) Task Group (TG) has been working on proposing a series of standards to provide Ethernet with hard real-time guarantees, online management of the traffic and fault tolerance mechanisms. In this way the TG expects to create [...] Read more.
In recent years the Time-Sensitive Networking (TSN) Task Group (TG) has been working on proposing a series of standards to provide Ethernet with hard real-time guarantees, online management of the traffic and fault tolerance mechanisms. In this way the TG expects to create the network technology of future novel applications with real-time and reliability requirements. TSN proposes using spatial redundancy to increase the reliability of Ethernet networks, but using spatial redundancy to tolerate temporary faults is not a cost-effective solution. For this reason, we propose to use time redundancy to tolerate temporary faults in the links of TSN-based networks. Specifically, we have proposed the Proactive Transmission of Replicated Frames (PTRF) mechanism, which consists in transmitting several copies of each frame in a preventive manner. In this article we present for the first time a detailed description of the mechanism, with the three different approaches we have designed. We also present the implementation of PTRF in a real TSN prototype. Furthermore, we carry out a qualitative comparison of the different approaches of the mechanism and we experimentally evaluate the approaches of the mechanism in a quantitative manner from three perspectives: the end-to-end delay, the jitter and the bandwidth consumption. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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<p>A communication cycle example divided into a protected window, an unprotected window and a guard band.comm-cycle</p>
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<p>Internal structure of a port with Time-Aware Shaper. Each egress queue of the port has a gate that can be configured as open “O”, to allow frames to be transmitted, or closed “c”, to prevent frames from being transmitted. On the left-hand side of the figure we can see an example of gate control list.</p>
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<p>An example of a TSN-based network architecture with four end-systems and six bridges in a mesh topology. End-systems are represented with squares, bridges with circles and links with arrows. The T means that the end-system is a talker, while the L means it is a listener.</p>
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<p>Topologies used to study the number of simultaneous permanent and temporary faults that can be tolerated using spatial redundancy solely.</p>
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<p>Behaviour of the approach A of the PTRF mechanism in the presence of temporary faults in the links.</p>
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<p>Behaviour of the approach B of the PTRF mechanism in the presence of temporary faults in the links.</p>
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<p>Behaviour of the approach C of the PTRF mechanism in the presence of temporary faults in the links.</p>
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<p>Example of interleaving replicas when using the approach A of the PTRF mechanism to tolerate temporary faults in the links.</p>
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<p>Functions of the PTRF mechanism in reception and transmission.</p>
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<p>Format of an IEEE 802.1Q Ethernet Data frame that has been replicated using PTRF. As we can see highlighted in blue, the frame conveys new fields, namely the PTRF Ethertype, PTRF frame identifier and the expected number of replicas.</p>
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<p>This figure shows the operation of the PTRF Replica Identification table. Specifically, on the top of the Figure, we see how the table processes a replica of a message edition that has previously been received. On the bottom, we see how the table processes a replica of a message edition that is received for the first time.</p>
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<p>Basic architecture of the MPSoC that implements the PTRF mechanism. The PTRF components are highlighted in blue. Solid lines represent the path which frames follow whereas dashed lines represent management or configuration interactions.</p>
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<p>Topology used to measure the end-to-end delay in the absence of faults.</p>
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<p>This figure shows the end-to-end delay of four replicas transmitted through a link between two bridges (Sw1 and Sw2). Specifically, the figure shows the transmission of four replicas with their respective maximum end-to-end delays marked with black arrows, i.e., e2e1 is the maximum end-to-end delay when transmitting a single replica, e2e2 is the maximum end-to-end delay when transmitting two replicas, etc. Moreover, the blue arrows show the variation between the end-to-end delays, a.k.a. jitter.</p>
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<p>Topology used to measure the end-to-end delay and the jitter in the presence of faults.</p>
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<p>Mean value for the maximum end-to-end delay for approaches A and B with <span class="html-italic">k</span> = <span class="html-italic">k</span>’ = 1, 2, 3 and 4. The X axis represent the frame length in bytes, while the Y axis represent the end-to-end delay in nanoseconds.</p>
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<p>Topology used to measure the jitter in the absence of faults.</p>
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20 pages, 10038 KiB  
Article
Improving the Ambient Temperature Control Performance in Smart Homes and Buildings
by Fernando Fontes, Rómulo Antão, Alexandre Mota and Paulo Pedreiras
Sensors 2021, 21(2), 423; https://doi.org/10.3390/s21020423 - 9 Jan 2021
Cited by 8 | Viewed by 3608
Abstract
Currently, it is becoming increasingly common to find numerous electronic devices installed in office and residential spaces as part of building automation solutions. These devices provide a rich set of data related to the inside and outside environment, such as indoor and outdoor [...] Read more.
Currently, it is becoming increasingly common to find numerous electronic devices installed in office and residential spaces as part of building automation solutions. These devices provide a rich set of data related to the inside and outside environment, such as indoor and outdoor temperature, humidity, and solar radiation. However, commercial of-the-shelf climatic control systems continue to rely on simple controllers like proportional-integral-derivative or even on-off, which do not take into account such variables. This work evaluates the potential performance gains of adopting more advanced controllers, in this case based on pole-placement, enhanced with additional variables, namely solar radiation and external temperature, obtained with dedicated low-cost sensors. This approach is evaluated both in simulated and real-world environments. The obtained results show that pole-placement controllers clearly outperform on-off controllers and that the use of the additional variables in pole-placement controllers allows relevant performance gains in key parameters such as error signal MSE (17%) and control signal variance (40%), when compared with simple PP controllers. The observed energy consumption savings obtained by using the additional variables are marginal (≈1%, but the reduction of the error signal MSE and control signal variance have a significant impact on energy consumption peaks and on equipment lifetime, thus largely compensating the increase in the system complexity. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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<p>Illustration of the developed indoor environment model.</p>
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<p>(<b>a</b>) Room used for the thermostatic control system evaluation; (<b>b</b>) photovoltaic panel used for the measurement of the solar radiation.</p>
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<p>Illustrative diagram of the actuator system used during the real-word evaluation.</p>
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<p>PV panel performance after calibration.</p>
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<p>Step response of the dynamic model.</p>
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<p>House temperature variation during the simulation time for PP and on-off controllers.</p>
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<p>External environment conditions during the simulation time.</p>
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<p>House temperature variation during the simulation time for two different implementations of the PP controller.</p>
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<p>Step response of the system [<a href="#B5-sensors-21-00423" class="html-bibr">5</a>].</p>
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<p>House temperature variation during test time on-off [<a href="#B5-sensors-21-00423" class="html-bibr">5</a>].</p>
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<p>External environment conditions during test time on-off [<a href="#B5-sensors-21-00423" class="html-bibr">5</a>].</p>
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<p>House temperature variation during test time PP [<a href="#B5-sensors-21-00423" class="html-bibr">5</a>].</p>
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<p>External environment conditions during test time PP [<a href="#B5-sensors-21-00423" class="html-bibr">5</a>].</p>
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20 pages, 884 KiB  
Article
A New Association Scheme for Handling Node Mobility in Cluster-Tree Wireless Sensor Networks
by Rogério Casagrande, Ricardo Moraes, Carlos Montez, Francisco Vasques and Erico Leão
Sensors 2020, 20(19), 5694; https://doi.org/10.3390/s20195694 - 6 Oct 2020
Cited by 1 | Viewed by 1995
Abstract
Node mobility in multi-hop communication environments is an important feature of Wireless Sensor Network (WSN)-based monitoring systems. It allows nodes to have freedom of movement, without being restricted to a single-hop communication range. In IEEE 802.15.4 WSNs, nodes are only able to transfer [...] Read more.
Node mobility in multi-hop communication environments is an important feature of Wireless Sensor Network (WSN)-based monitoring systems. It allows nodes to have freedom of movement, without being restricted to a single-hop communication range. In IEEE 802.15.4 WSNs, nodes are only able to transfer data messages after completing a connection with a coordinator through an association mechanism. Within this context, a handover procedure needs to be executed by a mobile node whenever there is a disconnection from a coordinator and the establishment of a connection to another one. Many applications, such as those found in health monitoring systems, strongly need support for node mobility without loss of data during the handover. However, it has been observed that the time required to execute the handover procedure is one of the main reasons why IEEE 802.15.4 cannot fully support mobility. This paper proposes an improvement to this procedure using a set of combined strategies, such as anticipation of both the handover mechanism and the scan phase enhancement. Simulations show that it is possible to reduce latency during the association and re-association processes, making it feasible to develop WSN-based distributed monitoring systems with mobile nodes and stringent time constraints. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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<p>Improvements in the association process.</p>
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<p>Synthesis of the reported proposals.</p>
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<p>Bottom-up cluster active period scheduling.</p>
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<p>Node speed analysis.</p>
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<p>Message sequence chart for enhanced association mechanism.</p>
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<p>Communication scenario.</p>
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<p>Average packet delivery rate at 1.4 m/s.</p>
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<p>Average percentage of disconnect time at 1.4 m/s.</p>
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<p>Average end-to-end delay at 1.4 m/s.</p>
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<p>Average packet delivery rate at 5 m/s.</p>
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<p>Average percentage of disconnect time at 5 m/s.</p>
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<p>Average end-to-end delay at 5 m/s.</p>
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30 pages, 1284 KiB  
Article
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
by Ivan Pisa, Antoni Morell, Jose Lopez Vicario and Ramon Vilanova
Sensors 2020, 20(13), 3743; https://doi.org/10.3390/s20133743 - 4 Jul 2020
Cited by 30 | Viewed by 5903
Abstract
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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<p>Benchmark Simulation Model N.1 (BSM1) model for a biological wastewater treatment plant (WWTP). <math display="inline"><semantics> <msub> <mi>Q</mi> <mi>a</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>Q</mi> <mi>r</mi> </msub> </semantics></math> are the internal and external recirculation flow rates.</p>
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<p>Internal Model Controller (IMC). <math display="inline"><semantics> <mrow> <mi>r</mi> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </semantics></math> corresponds to the reference signal, <math display="inline"><semantics> <mrow> <msup> <mi>e</mi> <mo>′</mo> </msup> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> to the mismatch between the output of the real process and <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>d</mi> <mi>i</mi> <mi>r</mi> </mrow> </msub> <mrow> <mo>(</mo> <mi>z</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> output (<math display="inline"><semantics> <mrow> <mover accent="true"> <mi>y</mi> <mo>^</mo> </mover> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>).</p>
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<p>Long short term memory (LSTM) cell structure.</p>
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<p>LSTM Architectures considered in the LSTM-based IMC controller. <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>O</mi> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>O</mi> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>H</mi> <mo>,</mo> <mn>4</mn> </mrow> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> are the oxygen, the nitrate-nitrogen and the ammonium concentrations in the fourth reactor tank.</p>
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<p>Mutual Information between input data. The other concentrations corresponds to BSM1 influent concentrations: the soluble and particulate inert organic matter concentration (<math display="inline"><semantics> <msub> <mi>S</mi> <mi>I</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mi>I</mi> </msub> </semantics></math>), the readily and slowly biodegradable substrate concentration (<math display="inline"><semantics> <msub> <mi>S</mi> <mi>S</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mi>S</mi> </msub> </semantics></math>), the active heterotrophic and autotrophic biomass concentration (<math display="inline"><semantics> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>H</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mrow> <mi>B</mi> <mi>A</mi> </mrow> </msub> </semantics></math>), the concentration of particulate products arising from biomass decay (<math display="inline"><semantics> <msub> <mi>X</mi> <mi>P</mi> </msub> </semantics></math>), the soluble and particulate biodegradable organic nitrogen concentration (<math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>N</mi> <mi>D</mi> </mrow> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>X</mi> <mrow> <mi>N</mi> <mi>D</mi> </mrow> </msub> </semantics></math>), the alkalinity (<math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>A</mi> <mi>L</mi> <mi>K</mi> </mrow> </msub> </semantics></math>) and the input flow rate (<math display="inline"><semantics> <msub> <mi>Q</mi> <mi>i</mi> </msub> </semantics></math>).</p>
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<p>Ideal LSTM architectures with the Denoising Stage.</p>
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<p>Denoising Autoencoder (DAE) architectures. <math display="inline"><semantics> <mrow> <mi mathvariant="bold">x</mi> <mo>∈</mo> <msup> <mi mathvariant="double-struck">R</mi> <mrow> <mi>m</mi> <mo>×</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> corresponds to the input data. <math display="inline"><semantics> <mrow> <mi mathvariant="bold">w</mi> <mo>∈</mo> <msup> <mi mathvariant="double-struck">R</mi> <mrow> <mi>m</mi> <mo>×</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> corresponds to the noise corrupting the input data vector. <math display="inline"><semantics> <mrow> <mi mathvariant="bold">z</mi> <mo>∈</mo> <msup> <mi mathvariant="double-struck">R</mi> <mrow> <mi>k</mi> <mo>×</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> vector corresponds to the compressed data which is represented in a space with k dimensions. Finally, <math display="inline"><semantics> <mrow> <mi mathvariant="bold">x</mi> <mo>∈</mo> <msup> <mi mathvariant="double-struck">R</mi> <mrow> <mi>m</mi> <mo>×</mo> <mn>1</mn> </mrow> </msup> </mrow> </semantics></math> is the denoised data vector. Nodes in red correspond to the DAE latent space.</p>
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<p>Training curves of the <math display="inline"><semantics> <mrow> <mi>M</mi> <mi>L</mi> <mi>P</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>L</mi> <mi>S</mi> <mi>T</mi> <mi>M</mi> </mrow> </semantics></math> structures. Notice that neither of them reach the 500 epochs due to the application of Early Stopping.</p>
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<p>(<b>a</b>) shows the stability analysis of the Artificial Neural Network (ANN)-based Internal Model Controller (IMC) for different <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>c</mi> </msub> </semantics></math>. Those frequencies showing a color are the ones where the ANN-based IMC fails the stability test. (<b>b</b>) shows the frequency response of the controlled variable <math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>O</mi> <mo>,</mo> <mn>5</mn> </mrow> </msub> </semantics></math> and the actuation variable <math display="inline"><semantics> <msub> <mi>K</mi> <mrow> <mi>L</mi> <mi>a</mi> <mo>,</mo> <mn>5</mn> </mrow> </msub> </semantics></math>. Notice that the stability limit (vertical red line) corresponds to the one obtained when a cut-off frequency (<math display="inline"><semantics> <msub> <mi>ω</mi> <mi>c</mi> </msub> </semantics></math>) equal to <math display="inline"><semantics> <mrow> <msub> <mi>ω</mi> <mi>c</mi> </msub> <mo>=</mo> <mn>10</mn> <mo>·</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> <mspace width="0.166667em"/> </mrow> </semantics></math> rad/s.</p>
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<p>Evolution of Integrated Absolute Error (IAE) and Integrated Squared Error (ISE) as a function of <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>c</mi> </msub> </semantics></math>. The different values of IAE and ISE are computed as a function of the <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>c</mi> </msub> </semantics></math>. The best <math display="inline"><semantics> <msub> <mi>ω</mi> <mi>c</mi> </msub> </semantics></math> is the one whose IAE and ISE values are close to 0. Notice that this point yields better IAE and ISE values than the default PI controller (∘).</p>
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<p><math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>O</mi> <mo>,</mo> <mn>5</mn> </mrow> </msub> </semantics></math> variable set-point. From now on, this set-point is the one considered to obtain all the results that attain this work. Only from day 7 to 14 are shown since they are the days considered in the performance computation.</p>
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<p>Control behaviour of the LSTM-based IMC controller (controlled and actuation variables are shown in (<b>a</b>) and (<b>b</b>), respectively). Dry, rainy and stormy weathers have been considered. Notice that only those days where the behaviour of the LSTM-based controller varies are shown.</p>
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<p>Stability analysis of the LSTM-based prediction structure retrained with noisy data.</p>
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<p><math display="inline"><semantics> <msub> <mi>S</mi> <mrow> <mi>O</mi> <mo>,</mo> <mn>4</mn> </mrow> </msub> </semantics></math> denoising process. The measured signal is depicted in blue, the denoised and real ones are shown in orange and yellow, respectively.</p>
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<p>Stability analysis for the different denoising approaches.</p>
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<p>Tracking process of the <math display="inline"><semantics> <mrow> <msub> <mi>S</mi> <mrow> <mi>O</mi> <mo>,</mo> <mn>5</mn> </mrow> </msub> <mrow> <mo>[</mo> <mi>n</mi> <mo>]</mo> </mrow> </mrow> </semantics></math> concentration. The three different denoising approaches have been considered, however, the one offering the best tracking is the Multilayer Perceptron-Sliding Window (MLP-SW).</p>
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Review

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22 pages, 8576 KiB  
Review
Industry 4.0: A Proposal of Paradigm Organization Schemes from a Systematic Literature Review
by Cristian Rocha-Jácome, Ramón González Carvajal, Fernando Muñoz Chavero, Esteban Guevara-Cabezas and Eduardo Hidalgo Fort
Sensors 2022, 22(1), 66; https://doi.org/10.3390/s22010066 - 23 Dec 2021
Cited by 12 | Viewed by 5503
Abstract
Currently, the concept of Industry 4.0 is well known; however, it is extremely complex, as it is constantly evolving and innovating. It includes the participation of many disciplines and areas of knowledge as well as the integration of many technologies, both mature and [...] Read more.
Currently, the concept of Industry 4.0 is well known; however, it is extremely complex, as it is constantly evolving and innovating. It includes the participation of many disciplines and areas of knowledge as well as the integration of many technologies, both mature and emerging, but working in collaboration and relying on their study and implementation under the novel criteria of Cyber–Physical Systems. This study starts with an exhaustive search for updated scientific information of which a bibliometric analysis is carried out with results presented in different tables and graphs. Subsequently, based on the qualitative analysis of the references, we present two proposals for the schematic analysis of Industry 4.0 that will help academia and companies to support digital transformation studies. The results will allow us to perform a simple alternative analysis of Industry 4.0 to understand the functions and scope of the integrating technologies to achieve a better collaboration of each area of knowledge and each professional, considering the potential and limitations of each one, supporting the planning of an appropriate strategy, especially in the management of human resources, for the successful execution of the digital transformation of the industry. Full article
(This article belongs to the Special Issue Sensor Applications in Industrial Automation)
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Figure 1

Figure 1
<p>The main steps taken to validate the research.</p>
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<p>(<b>a</b>) Ratio of exclusion and inclusion of documents of each paradigm and key technology; (<b>b</b>) Percentage of documents included and excluded with respect to the total.</p>
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<p>Geographical distribution and density of publications.</p>
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<p>Percentage of countries’ contribution.</p>
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<p>Chronology of scientific information on each technology and paradigm.</p>
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<p>(<b>a</b>) Percentage ratio of scientific production by years; (<b>b</b>) distribution of scientific information by year.</p>
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<p>Keywords most frequently used.</p>
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<p>Leading journals in this work.</p>
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<p>Language ratio.</p>
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<p>Proposed scheme of reorganization of Industry 4.0 criteria, paradigms, and key technologies.</p>
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<p>Proposed CSCW matrix for Industry 4.0 paradigms and key technologies.</p>
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