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Special Issue "Sensors, Robots, Internet of Things, and Smart Factories"

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

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Prof. Dr. Jehn-Ruey Jiang

Department of Computer Science and Information Engineering National Central University, Jhongli City 32001, Taiwan
Website | E-Mail
Phone: +886 3 4227151
Interests: wireless sensor networks; Internet of Things; cyber-physical systems; smart manufacturing; deep learning
Guest Editor
Prof. Dr. Tharek Abd Rahman

Universiti Teknologi Malaysia, Malaysia
Website | E-Mail
Phone: +607-5535305
Interests: radio propagation; antenna and RF design; indoors and outdoords wireless communication; 5G communication
Guest Editor
Prof. Dr. Haibo Zhang

Department of Computer Science, University of Otago, Dunedin 9054, New Zealand
Website | E-Mail
Phone: +64 3 479 8534
Interests: wireless sensor networks; routing protocol design; Internet of Things; cyber-physical systems; 5G networks; vehicular networks

Special Issue Information

Dear Colleagues,

Smart factories play an important role in the Industry 4.0 concept, which has been attracting much attention. With the advance of technologies of sensors, robots, and Internet of Things (IoT), a cyberphysical system (CPS) is built for gathering and analyzing sensor data, as well as for intelligently controlling robots, in order to meet smart factories’ requirements, such as performance optimization, energy efficiency, and dependability. You are invited to contribute to this Special Issue research results related to sensors, robots, and IoT for forming smart factories. Related research includes embedding of sensors (e.g., vision sensors, vibration sensors, and even RFID sensors/readers) and their applications (e.g., automated optical inspection or AOI), robots coordination and applications (e.g., path planning and navigation of automatic guided vehicles or AGV), networking for industry (e.g., fieldbus networking, industrial wireless networking, and 5G IoT networking), and big data storing and analysis (e.g., big data storing and retrieving, machine learning, deep learning).

Prof. Dr. Jehn-Ruey Jiang
Prof. Dr. Tharek Abd Rahman
Prof. Dr. Haibo Zhang
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 sensor data acquisition and fusion
  • RFID sensors/readers and related applications
  • Positioning in wireless networks
  • Fieldbus networking
  • 5G IoT networking
  • Intelligent robot coordination and control  
  • AGV navigation and path planning
  • Time series anomaly detection
  • Predictive and prescriptive maintenance
  • Machine learning/deep learning for smart manufacturing
  • Machine health prognostics (MHP)
  • Automated optical inspection (AOI)

Published Papers (3 papers)

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Research

Open AccessArticle RGB-D-Based Pose Estimation of Workpieces with Semantic Segmentation and Point Cloud Registration
Sensors 2019, 19(8), 1873; https://doi.org/10.3390/s19081873 (registering DOI)
Received: 4 March 2019 / Revised: 11 April 2019 / Accepted: 18 April 2019 / Published: 19 April 2019
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Abstract
As an important part of a factory’s automated production line, industrial robots can perform a variety of tasks by integrating external sensors. Among these tasks, grasping scattered workpieces on the industrial assembly line has always been a prominent and difficult point in robot [...] Read more.
As an important part of a factory’s automated production line, industrial robots can perform a variety of tasks by integrating external sensors. Among these tasks, grasping scattered workpieces on the industrial assembly line has always been a prominent and difficult point in robot manipulation research. By using RGB-D (color and depth) information, we propose an efficient and practical solution that fuses the approaches of semantic segmentation and point cloud registration to perform object recognition and pose estimation. Different from objects in an indoor environment, the characteristics of the workpiece are relatively simple; thus, we create and label an RGB image dataset from a variety of industrial scenarios and train the modified FCN (Fully Convolutional Network) on a homemade dataset to infer the semantic segmentation results of the input images. Then, we determine the point cloud of the workpieces by incorporating the depth information to estimate the real-time pose of the workpieces. To evaluate the accuracy of the solution, we propose a novel pose error evaluation method based on the robot vision system. This method does not rely on expensive measuring equipment and can also obtain accurate evaluation results. In an industrial scenario, our solution has a rotation error less than two degrees and a translation error < 10 mm. Full article
(This article belongs to the Special Issue Sensors, Robots, Internet of Things, and Smart Factories)
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Open AccessArticle PlantTalk: A Smartphone-Based Intelligent Hydroponic Plant Box
Sensors 2019, 19(8), 1763; https://doi.org/10.3390/s19081763
Received: 18 March 2019 / Revised: 7 April 2019 / Accepted: 10 April 2019 / Published: 12 April 2019
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Abstract
This paper proposes an IoT-based intelligent hydroponic plant factory solution called PlantTalk. The novelty of our approach is that the PlantTalk intelligence can be built through an arbitrary smartphone. We show that PlantTalk can flexibly configure the connections of various plant sensors and [...] Read more.
This paper proposes an IoT-based intelligent hydroponic plant factory solution called PlantTalk. The novelty of our approach is that the PlantTalk intelligence can be built through an arbitrary smartphone. We show that PlantTalk can flexibly configure the connections of various plant sensors and actuators through a smartphone. One can also conveniently write Python programs for plant-care intelligence through the smart phone. The developed plant-care intelligence includes automatic LED lighting, water spray, water pump and so on. As an example, we show that the PlantTalk intelligence effectively lowers the CO2 concentration, and the reduction speed is 53% faster than a traditional plant system. PlantTalk has been extended for a plant factory called AgriTalk. Full article
(This article belongs to the Special Issue Sensors, Robots, Internet of Things, and Smart Factories)
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Open AccessArticle Energy and Distance-Aware Hopping Sensor Relocation for Wireless Sensor Networks
Sensors 2019, 19(7), 1567; https://doi.org/10.3390/s19071567
Received: 9 February 2019 / Revised: 20 March 2019 / Accepted: 23 March 2019 / Published: 1 April 2019
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Abstract
Recent advances in big data technology collecting and analyzing large amounts of valuable data have attracted a lot of attention. When the information in non-reachable areas is required, IoT wireless sensor network technologies have to be applied. Sensors fundamentally have energy limitations, and [...] Read more.
Recent advances in big data technology collecting and analyzing large amounts of valuable data have attracted a lot of attention. When the information in non-reachable areas is required, IoT wireless sensor network technologies have to be applied. Sensors fundamentally have energy limitations, and it is almost impossible to replace energy-depleted sensors that have been deployed in an inaccessible region. Therefore, moving healthy sensors into the sensing hole will recover the faulty sensor area. In rough surfaces, hopping sensors would be more appropriate than wheel-driven mobile sensors. Sensor relocation algorithms to recover sensing holes have been researched variously in the past. However, the majority of studies to date have been inadequate in reality, since they are nothing but theoretical studies which assume that all the topology in the network is known and then computes the shortest path based on the nonrealistic backing up knowledge—The topology information. In this paper, we first propose a distributed hopping sensor relocation protocol. The possibility of movement of the hopping sensor is also considered to recover sensing holes and is not limited to applying the shortest path strategy. Finally, a performance analysis using OMNeT++ has demonstrated the solidification of the excellence of the proposed protocol. Full article
(This article belongs to the Special Issue Sensors, Robots, Internet of Things, and Smart Factories)
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