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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 (7 papers)

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Research

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Open AccessArticle A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks
Sensors 2017, 17(8), 1797; doi:10.3390/s17081797
Received: 27 June 2017 / Revised: 25 July 2017 / Accepted: 28 July 2017 / Published: 4 August 2017
PDF Full-text (1981 KB) | HTML Full-text | XML Full-text
Abstract
With the prevalence of low-power wireless devices in industrial applications, concerns about timeliness and reliability are bound to continue despite the best efforts of researchers to design Industrial Wireless Sensor Networks (IWSNs) to improve the performance of monitoring and control systems. As mobile
[...] Read more.
With the prevalence of low-power wireless devices in industrial applications, concerns about timeliness and reliability are bound to continue despite the best efforts of researchers to design Industrial Wireless Sensor Networks (IWSNs) to improve the performance of monitoring and control systems. As mobile devices have a major role to play in industrial production, IWSNs should support mobility. However, research on mobile IWSNs and practical tests have been limited due to the complicated resource scheduling and rescheduling compared with traditional wireless sensor networks. This paper proposes an effective mechanism to guarantee the performance of handoff, including a mobility-aware scheme, temporary connection and quick registration. The main contribution of this paper is that the proposed mechanism is implemented not only in our testbed but in a real industrial environment. The results indicate that our mechanism not only improves the accuracy of handoff triggering, but also solves the problem of ping-pong effect during handoff. Compared with the WirelessHART standard and the RSSI-based approach, our mechanism facilitates real-time communication while being more reliable, which can help end-to-end packet delivery remain an average of 98.5% in the scenario of mobile IWSNs. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Open AccessArticle Scheduling for Emergency Tasks in Industrial Wireless Sensor Networks
Sensors 2017, 17(7), 1674; doi:10.3390/s17071674
Received: 12 June 2017 / Revised: 17 July 2017 / Accepted: 18 July 2017 / Published: 20 July 2017
PDF Full-text (1065 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks (WSNs) are widely applied in industrial manufacturing systems. By means of centralized control, the real-time requirement and reliability can be provided by WSNs in industrial production. Furthermore, many approaches reserve resources for situations in which the controller cannot perform centralized
[...] Read more.
Wireless sensor networks (WSNs) are widely applied in industrial manufacturing systems. By means of centralized control, the real-time requirement and reliability can be provided by WSNs in industrial production. Furthermore, many approaches reserve resources for situations in which the controller cannot perform centralized resource allocation. The controller assigns these resources as it becomes aware of when and where accidents have occurred. However, the reserved resources are limited, and such incidents are low-probability events. In addition, resource reservation may not be effective since the controller does not know when and where accidents will actually occur. To address this issue, we improve the reliability of scheduling for emergency tasks by proposing a method based on a stealing mechanism. In our method, an emergency task is transmitted by stealing resources allocated to regular flows. The challenges addressed in our work are as follows: (1) emergencies occur only occasionally, but the industrial system must deliver the corresponding flows within their deadlines when they occur; (2) we wish to minimize the impact of emergency flows by reducing the number of stolen flows. The contributions of this work are two-fold: (1) we first define intersections and blocking as new characteristics of flows; and (2) we propose a series of distributed routing algorithms to improve the schedulability and to reduce the impact of emergency flows. We demonstrate that our scheduling algorithm and analysis approach are better than the existing ones by extensive simulations. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Open AccessArticle Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines
Sensors 2017, 17(7), 1610; doi:10.3390/s17071610
Received: 17 May 2017 / Revised: 30 June 2017 / Accepted: 4 July 2017 / Published: 11 July 2017
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Abstract
Existing transmission power grids suffer from high maintenance costs and scalability issues along with a lack of effective and secure system monitoring. To address these problems, we propose to use Wireless Sensor Networks (WSNs) as a technology to achieve energy efficient, reliable, and
[...] Read more.
Existing transmission power grids suffer from high maintenance costs and scalability issues along with a lack of effective and secure system monitoring. To address these problems, we propose to use Wireless Sensor Networks (WSNs) as a technology to achieve energy efficient, reliable, and low-cost remote monitoring of transmission grids. With WSNs, smart grid enables both utilities and customers to monitor, predict and manage energy usage effectively and react to possible power grid disturbances in a timely manner. However, the increased application of WSNs also introduces new security challenges, especially related to privacy, connectivity, and security management, repeatedly causing unpredicted expenditures. Monitoring the status of the power system, a large amount of sensors generates massive amount of sensitive data. In order to build an effective Wireless Sensor Network (WSN) for a smart grid, we focus on designing a methodology of efficient and secure delivery of the data measured on transmission lines. We perform a set of simulations, in which we examine different routing algorithms, security mechanisms and WSN deployments in order to select the parameters that will not affect the delivery time but fulfill their role and ensure security at the same time. Furthermore, we analyze the optimal placement of direct wireless links, aiming at minimizing time delays, balancing network performance and decreasing deployment costs. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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Open AccessArticle Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
Sensors 2017, 17(7), 1527; doi:10.3390/s17071527
Received: 26 April 2017 / Revised: 9 June 2017 / Accepted: 20 June 2017 / Published: 29 June 2017
PDF Full-text (636 KB) | HTML Full-text | XML Full-text
Abstract
Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy
[...] Read more.
Wireless sensor networks are required in smart applications to provide accurate control, where the high density of sensors brings in a large quantity of redundant data. In order to reduce the waste of limited network resources, data aggregation is utilized to avoid redundancy forwarding. However, most of aggregation schemes reduce information accuracy and prolong end-to-end delay when eliminating transmission overhead. In this paper, we propose a data aggregation scheme based on overlapping rate of sensing area, namely AggOR, aiming for energy-efficient data collection in wireless sensor networks with high information accuracy. According to aggregation rules, gathering nodes are selected from candidate parent nodes and appropriate neighbor nodes considering a preset threshold of overlapping rate of sensing area. Therefore, the collected data in a gathering area are highly correlated, and a large amount of redundant data could be cleaned. Meanwhile, AggOR keeps the original entropy by only deleting the duplicated data. Experiment results show that compared with others, AggOR has a high data accuracy and a short end-to-end delay with a similar network lifetime. Full article
(This article belongs to the Special Issue Smart Industrial Wireless Sensor Networks)
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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
PDF Full-text (3443 KB) | HTML Full-text | XML Full-text
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

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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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