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Evaluating Quality of Experience and Real- Time Performance of Industrial Internet of Things

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Evaluating Quality of Experience and Real- Time Performance of Industrial Internet of Things Evaluating Quality of Experience and real- time performance of Industrial Internet of Things Roman Zhohov Computer Science and Engineering, master's level (120 credits) 2018 Luleå University of Technology Department of Computer Science, Electrical and Space Engineering Lappeenranta University of Technology School of Engineering Science Degree Program in Computer Science Roman Zhohov EVALUATING QUALITY OF EXPERIENCE AND REAL-TIME PERFORMANCE OF INDUSTRIAL INTERNET OF THINGS Examiners: Professor Eric Rondeau, University of Lorraine Professor Jari Porras, Lappeenranta University of Technology Professor Karl Andersson, Lulea University of Technology Supervisors: Professor Karl Andersson, Lulea University of Technology Per Johansson, InfoVista AB, Skelleftea, Sweden Dimitar Minovski, Lulea University of Technology ABSTRACT Lappeenranta University of Technology School of Engineering Science Degree Program in Computer Science Roman Zhohov Evaluating Quality of Experience and real-time performance of Industrial Internet of Things Master’s Thesis 46 pages, 16 figures, 3 tables Examiners: Professor Eric Rondeau, University of Lorraine Professor Jari Porras, Lappeenranta University of Technology Professor Karl Andersson, Lulea University of Technology Keywords: QoE, Industry 4.0, QoS, real-time communications, IIoT, CPS. The Industrial Internet of Things (IIoT) is one of the key technologies of Industry 4.0 that will be an integral part of future smart and sustainable production. The current constituted models for estimating Quality of Experience (QoE) are mainly targeting the multimedia systems. Present models for evaluating QoE, specifically leveraged by the expensive subjective tests, are not applicable for IIoT applications. This work triggers the discussion on defining the QoE domain for IIoT services and applications. Industry-specific KPIs are proposed to assure QoE by linking business and technology domains. Tele-remote mining machines are considered as a case study for developing the QoE model by taking into account key challenges in QoE domain. As a result, QoE layered model is proposed, which as an outcome predicts the QoE of IIoT services and applications in a form of pre-defined Industrial KPIs. Moreover, software tool and analytical model is proposed to be used as an evaluation method for certain traffic types in the developed model. ii ACKNOWLEDGEMENTS I would like to thank my thesis advisor prof. Karl Andersson at Lulea University of Technology for providing a fantastic opportunity to work in such good environment and made my second year of PERCCOM master program less stressful. I must express gratitude to my supervisor Per Johansson, this work would be impossible without him. Without doubt, it was his work that inspired me to do my research. I am glad that he could always find the time for our endless discussions. Special thanks to Niklas Ögren whose comments have shaped this work. His expertise in the fields of wireless communications and protocols always made me to look at the problem from the different angle. I thank Dimitar Minovski who combined roles of colleague, co-author, flat-mate and advisor. It was a wonderful time that we shared, and I appreciate his help and support. I would also like to acknowledge TEMS team (Ulf Marklund, Magnus Furstenborg, Anna Lindberg and others) that provided me with great tools and vital help to create prototypes and develop ideas. Thanks to all my colleagues from InfoVista, it was a pure pleasure to work with them. I also thank prof. Sergey Bunin who was my supervisor during bachelor studies. My research work started under his supervision and I cannot overestimate how much I have learnt during my work with him. Finally, I thank my mother Olena Zhohova. Her endless love, support and care helped me to achieve everything that I have now. iii TABLE OF CONTENTS 1 INTRODUCTION ............................................................................................. 4 1.1 BACKGROUND......................................................................................................... 4 1.2 INDUSTRIAL CASE STUDY ........................................................................................ 5 1.3 MOTIVATION .......................................................................................................... 7 1.4 RESEARCH QUESTIONS ............................................................................................ 7 2 LITERATURE REVIEW ................................................................................... 8 3 METHODOLOGY .......................................................................................... 10 4 DEFINING QUALITY OF EXPERIENCE IN INDUSTRIAL INTERNET OF THINGS ................................................................................................................ 13 5 EVALUATING QUALITY OF EXPERIENCE IN INDUSTRIAL INTERNET OF THINGS ................................................................................................................ 18 5.1 QOE LAYERED-MODEL PROPOSAL ......................................................................... 18 5.1.1 Physical Layer ................................................................................................. 19 5.1.2 Network Layer .................................................................................................. 19 5.1.3 Service Layer ................................................................................................... 20 5.2 SYSTEM’S ARCHITECTURE AND TYPES OF TRAFFIC ................................................ 21 5.3 REAL-TIME SENSOR STREAM................................................................................ 21 5.4 REAL-TIME PERFORMANCE EVALUATION AND PREDICTION ................................... 23 5.4.1 Background and network architecture ............................................................ 23 5.4.2 Time synchronization ....................................................................................... 25 5.4.3 Experimental setup .......................................................................................... 28 5.4.4 Latency prediction ........................................................................................... 30 6 SUSTAINABILITY ASPECTS ....................................................................... 35 7 FUTURE WORK ............................................................................................ 39 8 CONCLUSION ............................................................................................... 40 9 REFERENCES .............................................................................................. 41 1 LIST OF SYMBOLS AND ABBREVIATIONS ADB Android Debug Bridge BER Bit error rate BLER Block error rate CPS Cyber-Physical System D2D Device-to-device EPC Evolved Packet Core EPS Evolved Packet System ICT Information and communication technology IIoT Industrial IoT IoT Internet of Things KPI Key performance indicator LTE Long Term Evolution M2M Machine-to-machine MIoT Multimedia IoT ML Machine Learning MOS Mean opinion score NTP Network time protocol OEE Overall equipment efficiency PEVQ Perceptual Evaluation of Video Quality POLQA Perceptual Objective Listening Quality Analysis PTP Precision time protocol QoC Quality of Context QoD Quality of Data QoE Quality of Experience QoN Quality of Network QoS Quality of Service RAN Radio Access Network RFID Radio-frequency identification RSRP Reference signal received power RSRQ Reference signal received quality 2 RSSI Received signal strength indicator RTOOL Real-time Tool RTT Round-trip time SINR Signal to interference plus noise ratio SLA Service-level Agreement SNR Signal to noise ratio UX User eXperience 3 1 INTRODUCTION The fourth industrial revolution is predicted a-priori and manifested as Industry 4.0. Smart Factories, Industrial Internet of Things (IIoT) and Cyber-Physical Systems (CPS) are main enabling components of Industry 4.0 that have tremendous effect on industrial application scenarios and automation [1]. Digitalizing of industrial processes will deliver an important boost in productivity and trigger economic growth [2]. Industry 4.0 transformations have attracted significant attention from academia and industry, it is reflected in the vast number of global projects and initiatives that are addressing IIoT concept. For instance, Industrie 4.0 project was accepted in “Action Plan High-tech strategy 2020” by German Federal Government in July 2010 [3]. Another example is the Industrial Internet Consortium (IIC) which is enabling and accelerating adoption of the Industrial Internet as an essential step to increase competitiveness in key industry sectors. According to the predictions implementation of IIoT will have a tremendous effect on the global economy, PwC’s 2016 Global Industry 4.0 survey respondents expect to see US$421 billion in cost reductions and US$493 billion in increased annual revenues p.a [4]. 1.1 Background The core of IIoT and CPS is essentially the robust exchange of information. Originally, conventional telecom networks could not cope with the industry-specific requirements for reliable, predictable and efficient communication. Industrial networks were mainly based on diverse deterministic bus technologies (controller area network – CAN, PROFIBUS, INTERBUS, etc.) to satisfy strict requirements of hard real-time automation systems. Development of industrial communication systems and networks is shown in Fig. 1, as one may notice industrial communication system move
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