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  • 1.
    Basir, Rabeea
    et al.
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan.
    Qaisar, Saad
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan.
    Ali, Mudassar
    School of Electrical Engineering and Computer Science, National University of Sciences and Technology, Islamabad 44000, Pakistan.
    Aldwairi, Monther
    College of Technological Innovation, Zayed University, Abu Dhabi 144534, UAE.
    Ashraf, Muhammad Ikram
    Centre for Wireless Communication, University of Oulu, 90014 Oulu, Finland.
    Mahmood, Aamir
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Gidlund, Mikael
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Fog Computing Enabling Industrial Internet of Things: State-of-the-Art and Research Challenges2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 21, article id 4807Article in journal (Refereed)
    Abstract [en]

    Industry is going through a transformation phase, enabling automation and data exchange in manufacturing technologies and processes, and this transformation is called Industry 4.0. Industrial Internet-of-Things (IIoT) applications require real-time processing, near-by storage, ultra-low latency, reliability and high data rate, all of which can be satisfied by fog computing architecture. With smart devices expected to grow exponentially, the need for an optimized fog computing architecture and protocols is crucial. Therein, efficient, intelligent and decentralized solutions are required to ensure real-time connectivity, reliability and green communication. In this paper, we provide a comprehensive review of methods and techniques in fog computing. Our focus is on fog infrastructure and protocols in the context of IIoT applications. This article has two main research areas: In the first half, we discuss the history of industrial revolution, application areas of IIoT followed by key enabling technologies that act as building blocks for industrial transformation. In the second half, we focus on fog computing, providing solutions to critical challenges and as an enabler for IIoT application domains. Finally, open research challenges are discussed to enlighten fog computing aspects in different fields and technologies.

  • 2.
    Düking, Peter
    et al.
    University of Würzburg, Würzburg, Germany.
    Achtzehn, Silvia
    German Sport University, Cologne, Germany.
    Holmberg, Hans-Christer
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. UiT The Arctic University of Norway, Tromso, Norway.
    Sperlich, Billy
    University of Würzburg, Würzburg, Germany.
    Integrated framework of load monitoring by a combination of smartphone applications, wearables and point-of-care testing provides feedback that allows individual responsive adjustments to activities of daily living2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1632Article in journal (Refereed)
    Abstract [en]

    Athletes schedule their training and recovery in periods, often utilizing a pre-defined strategy. To avoid underperformance and/or compromised health, the external load during training should take into account the individual’s physiological and perceptual responses. No single variable provides an adequate basis for planning, but continuous monitoring of a combination of several indicators of internal and external load during training, recovery and off-training as well may allow individual responsive adjustments of a training program in an effective manner. From a practical perspective, including that of coaches, monitoring of potential changes in health and performance should ideally be valid, reliable and sensitive, as well as time-efficient, easily applicable, non-fatiguing and as non-invasive as possible. Accordingly, smartphone applications, wearable sensors and point-of-care testing appear to offer a suitable monitoring framework allowing responsive adjustments to exercise prescription. Here, we outline 24-h monitoring of selected parameters by these technologies that (i) allows responsive adjustments of exercise programs, (ii) enhances performance and/or (iii) reduces the risk for overuse, injury and/or illness.

  • 3.
    Lavassani, Mehrzad
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Forsström, Stefan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Jennehag, Ulf
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Zhang, Tingting
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Combining Fog Computing with Sensor Mote Machine Learning for Industrial IoT2018In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 18, no 5, article id 1532Article in journal (Refereed)
    Abstract [en]

    Digitalization is a global trend becoming ever more important to our connected and sustainable society. This trend also affects industry where the Industrial Internet of Things is an important part, and there is a need to conserve spectrum as well as energy when communicating data to a fog or cloud back-end system. In this paper we investigate the benefits of fog computing by proposing a novel distributed learning model on the sensor device and simulating the data stream in the fog, instead of transmitting all raw sensor values to the cloud back-end. To save energy and to communicate as few packets as possible, the updated parameters of the learned model at the sensor device are communicated in longer time intervals to a fog computing system. The proposed framework is implemented and tested in a real world testbed in order to make quantitative measurements and evaluate the system. Our results show that the proposed model can achieve a 98% decrease in the number of packets sent over the wireless link, and the fog node can still simulate the data stream with an acceptable accuracy of 97%. We also observe an end-to-end delay of 180 ms in our proposed three-layer framework. Hence, the framework shows that a combination of fog and cloud computing with a distributed data modeling at the sensor device for wireless sensor networks can be beneficial for Industrial Internet of Things applications.

  • 4.
    Ma, Jian
    et al.
    Beijing Jiatong University, China.
    Yang, Dong
    Beijing Jiatong University, China.
    Zhang, Honke
    Beijing Jiatong University, China.
    Gidlund, Mikael
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    A Reliable Handoff Mechanism for Mobile Industrial Wireless Sensor Networks2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 8, article id 1797Article in journal (Refereed)
    Abstract [en]

    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.

  • 5.
    Shallari, Irida
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    O'Nils, Mattias
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
    From the Sensor to the Cloud: Intelligence Partitioning for Smart Camera Applications2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 23, article id 5162Article in journal (Refereed)
    Abstract [en]

    The Internet of Things has grown quickly in the last few years, with a variety of sensing, processing and storage devices interconnected, resulting in high data traffic. While some sensors such as temperature, or humidity sensors produce a few bits of data periodically, imaging sensors output data in the range of megabytes every second. This raises a complexity for battery operated smart cameras, as they would be required to perform intensive image processing operations on large volumes of data, within energy consumption constraints. By using intelligence partitioning we analyse the effects of different partitioning scenarios for the processing tasks between the smart camera node, the fog computing layer and cloud computing, in the node energy consumption as well as the real time performance of the WVSN (Wireless Vision Sensor Node). The results obtained show that traditional design space exploration approaches are inefficient for WVSN, while intelligence partitioning enhances the energy consumption performance of the smart camera node and meets the timing constraints.

  • 6.
    Stöggl, Thomas
    et al.
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Department of Sport Science and Kinesiology, University of SalzburgHallein/Rif, Austria .
    Holst, Anders
    School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden .
    Jonasson, Arndt
    Swedish Institute of Computer Science, Kista, Sweden.
    Andersson, Erik
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences.
    Wunsch, Thomas
    Department of Sport Science and Kinesiology, University of SalzburgHallein/Rif, Austria .
    Norström, Christer
    Swedish Institute of Computer Science, Kista, Sweden .
    Holmberg, Hans-Christer
    Mid Sweden University, Faculty of Human Sciences, Department of Health Sciences. Swedish Olympic Committee, Stockholm, Sweden .
    Automatic classification of the sub-techniques (gears) used in cross-country ski skating employing a mobile phone2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 11, p. 20589-20601Article in journal (Refereed)
    Abstract [en]

    The purpose of the current study was to develop and validate an automatic algorithm for classification of cross-country (XC) ski-skating gears (G) using Smartphone accelerometer data. Eleven XC skiers (seven men, four women) with regional-to-international levels of performance carried out roller skiing trials on a treadmill using fixed gears (G2left, G2right, G3, G4left, G4right) and a 950-m trial using different speeds and inclines, applying gears and sides as they normally would. Gear classification by the Smartphone (on the chest) and based on video recordings were compared. Formachine-learning, a collective database was compared to individual data. The Smartphone application identified the trials with fixed gears correctly in all cases. In the 950-m trial, participants executed 140 ± 22 cycles as assessed by video analysis, with the automatic Smartphone application giving a similar value. Based on collective data, gears were identified correctly 86.0% ± 8.9% of the time, a value that rose to 90.3% ± 4.1% (P < 0.01) with machine learning from individual data. Classification was most often incorrect during transition between gears, especially to or from G3. Identification was most often correct for skiers who made relatively few transitions between gears. The accuracy of the automatic procedure for identifying G2left, G2right, G3, G4left and G4right was 96%, 90%, 81%, 88% and 94%, respectively. The algorithm identified gears correctly 100% of the time when a single gear was used and 90% of the time when different gears were employed during a variable protocol. This algorithm could be improved with respect to identification of transitions between gears or the side employed within a given gear.

  • 7.
    Wang, Hongchao
    et al.
    Beijing Jiaotong University, Beijing, China.
    Ma, Jian
    Beijing Jiaotong University, Beijing, China.
    Yang, Dong
    Beijing Jiaotong University, Beijing, China.
    Gidlund, Mikael
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Information Systems and Technology.
    Efficient Resource Scheduling for Multipath Retransmission over Industrial WSAN Systems2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 18, article id 3927Article in journal (Refereed)
    Abstract [en]

    With recent adoption of Wireless Sensor-Actuator Networks (WSANs) in industrial automation, wireless control systems have emerged as a frontier of industrial networks. Hence, it has been shown that existing standards and researches concentrate on the reliability and real-time performance of WSANs. The multipath retransmission scheme with multiple channels is a key approach to guarantee the deterministic wireless communication. However, the efficiency of resource scheduling is seldom considered in applications with diverse data sampling rates. In this paper, we propose an efficient resources scheduling algorithm for multipath retransmission in WSANs. The objective of our algorithm is to improve efficiency and schedulability for the use of slot and channel resources. In detail, the proposed algorithm uses the approaches of CCA (clear channel assessment)-Embedded slot and Multiple sinks with Rate Monotonic scheme (CEM-RM) to decrease the number of collisions. We have simulated and implemented our algorithm in hardware and verified its performance in a real industrial environment. The achieved results show that the proposed algorithm significantly improves the schedulability without trading off reliability and real-time performance.

  • 8.
    Zhang, Renyun
    et al.
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Hummelgård, Magnus
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Ljunggren, Joel
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Chemical Engineering.
    Olin, Håkan
    Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences.
    Gold and Zno-Based Metal-Semiconductor Network for Highly Sensitive Room-Temperature Gas Sensing2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 18, article id 3815Article in journal (Refereed)
    Abstract [en]

    Metal-semiconductor junctions and interfaces have been studied for many years due to their importance in applications such as semiconductor electronics and solar cells. However, semiconductor-metal networks are less studied because there is a lack of effective methods to fabricate such structures. Here, we report a novel Au-ZnO-based metal-semiconductor (M-S)n network in which ZnO nanowires were grown horizontally on gold particles and extended to reach the neighboring particles, forming an (M-S)n network. The (M-S)n network was further used as a gas sensor for sensing ethanol and acetone gases. The results show that the (M-S)n network is sensitive to ethanol (28.1 ppm) and acetone (22.3 ppm) gases and has the capacity to recognize the two gases based on differences in the saturation time. This study provides a method for producing a new type of metal-semiconductor network structure and demonstrates its application in gas sensing.

1 - 8 of 8
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