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Special Issue "Smart Industrial Wireless Sensor Networks"

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

Deadline for manuscript submissions: 31 December 2017

Special Issue Editors

Guest Editor
Prof. Dr. Lei Shu

Guangdong University of Petrochemical Technology, China / University of Lincoln, UK
Website | E-Mail
Interests: wireless sensor networks; multimedia communication; middleware; security
Guest Editor
Prof. Dr. Gerhard P. Hancke

Department of Electrical, Electronic and Computer Engineering, University of Pretoria, South Africa
Website | E-Mail
Interests: wireless sensor networks
Guest Editor
Dr. Chunsheng Zhu

Department of Electrical and Computer Engineering, The University of British Columbia, Canada
Website | E-Mail
Interests: wireless sensor networks; cloud computing; Internet of Things; big data; social networks; security
Guest Editor
Dr. Mithun Mukherjee

Guangdong University of Petrochemical Technology, China
Website | E-Mail
Interests: wireless sensor network; wireless communications; energy harvesting; cloud computing

Special Issue Information

Dear Colleagues,

Researchers are in the pursuit of emerging technologies that enable human-centric or machine-centric networks to meet the evolving requirements (e.g., factory automation, fault diagnosis, fuel consumption monitoring, surveillance, etc.) in industries. Toward this goal, Smart Industrial Wireless Sensor Networks (SIWSNs) are identified as an essential technology. Particularly, on the one hand, with SIWSNs, data sensing, gathering and communication are performed intelligently by all kinds of industrial wireless sensors (e.g., photoelectric sensor, ultrasonic sensor, gas sensor, video sensor, etc.). Thus, various industrial data can be exchanged and managed autonomously and efficiently. On the other hand, due to the ease of deployment and flexibility of SIWSNs, the inherent drawbacks of wired industrial networks can be well overcome.

However, to utilize SIWSNs in a robust manner, there are a lot of tough issues to be addressed (e.g., coverage, localization, middleware, energy efficiency, quality of service, security, etc.). Moreover, with the recent adoption of cloud computing and big data technologies in industries, new issues might be posed for SIWSNs.

Therefore, this Special Issue aims to solicit high quality original technical or experimental papers which concern the current development or future challenge for SIWSNs. Review or survey papers will also be considered. Potential topics include, but are not limited to:

  • Wireless access for SIWSNs
  • Coverage for SIWSNs
  • Localization for SIWSNs
  • Data aggregation for SIWSNs
  • Data management for SIWSNs
  • Middleware for SIWSNs
  • Energy efficiency for SIWSNs
  • Quality of service for SIWSNs
  • Security for SIWSNs
  • Cloud computing for SIWSNs
  • Big data for SIWSNs

Prof. Dr. Lei Shu
Prof. Dr. Gerhard P. Hancke
Dr. Chunsheng Zhu
Dr. Mithun Mukherjee
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 monthly 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

  • Industrial sensor networks
  • Smartness
  • Wireless
  • Robustness
  • Cloud computing
  • Big data

Published Papers (3 papers)

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Research

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Open AccessArticle Compressed-Sensing Reconstruction Based on Block Sparse Bayesian Learning in Bearing-Condition Monitoring
Sensors 2017, 17(6), 1454; doi:10.3390/s17061454
Received: 24 March 2017 / Revised: 10 June 2017 / Accepted: 15 June 2017 / Published: 21 June 2017
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Abstract
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression
[...] Read more.
Remote monitoring of bearing conditions, using wireless sensor network (WSN), is a developing trend in the industrial field. In complicated industrial environments, WSN face three main constraints: low energy, less memory, and low operational capability. Conventional data-compression methods, which concentrate on data compression only, cannot overcome these limitations. Aiming at these problems, this paper proposed a compressed data acquisition and reconstruction scheme based on Compressed Sensing (CS) which is a novel signal-processing technique and applied it for bearing conditions monitoring via WSN. The compressed data acquisition is realized by projection transformation and can greatly reduce the data volume, which needs the nodes to process and transmit. The reconstruction of original signals is achieved in the host computer by complicated algorithms. The bearing vibration signals not only exhibit the sparsity property, but also have specific structures. This paper introduced the block sparse Bayesian learning (BSBL) algorithm which works by utilizing the block property and inherent structures of signals to reconstruct CS sparsity coefficients of transform domains and further recover the original signals. By using the BSBL, CS reconstruction can be improved remarkably. Experiments and analyses showed that BSBL method has good performance and is suitable for practical bearing-condition monitoring. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Open AccessArticle Public Auditing with Privacy Protection in a Multi-User Model of Cloud-Assisted Body Sensor Networks
Sensors 2017, 17(5), 1032; doi:10.3390/s17051032
Received: 9 March 2017 / Revised: 25 April 2017 / Accepted: 27 April 2017 / Published: 5 May 2017
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Abstract
Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs,
[...] Read more.
Wireless Body Sensor Networks (WBSNs) are gaining importance in the era of the Internet of Things (IoT). The modern medical system is a particular area where the WBSN techniques are being increasingly adopted for various fundamental operations. Despite such increasing deployments of WBSNs, issues such as the infancy in the size, capabilities and limited data processing capacities of the sensor devices restrain their adoption in resource-demanding applications. Though providing computing and storage supplements from cloud servers can potentially enrich the capabilities of the WBSNs devices, data security is one of the prevailing issues that affects the reliability of cloud-assisted services. Sensitive applications such as modern medical systems demand assurance of the privacy of the users’ medical records stored in distant cloud servers. Since it is economically impossible to set up private cloud servers for every client, auditing data security managed in the remote servers has necessarily become an integral requirement of WBSNs’ applications relying on public cloud servers. To this end, this paper proposes a novel certificateless public auditing scheme with integrated privacy protection. The multi-user model in our scheme supports groups of users to store and share data, thus exhibiting the potential for WBSNs’ deployments within community environments. Furthermore, our scheme enriches user experiences by offering public verifiability, forward security mechanisms and revocation of illegal group members. Experimental evaluations demonstrate the security effectiveness of our proposed scheme under the Random Oracle Model (ROM) by outperforming existing cloud-assisted WBSN models. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Review

Jump to: Research

Open AccessReview Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
Sensors 2017, 17(5), 1031; doi:10.3390/s17051031
Received: 3 March 2017 / Revised: 13 April 2017 / Accepted: 25 April 2017 / Published: 4 May 2017
PDF Full-text (543 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of
[...] Read more.
Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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