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  • 1.
    Minovski, Dimitar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Research challenges in Connecting Intenet of Things for Pervasive and Mobile Computing2017Report (Other academic)
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    fulltext
  • 2.
    Minovski, Dimitar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Towards Quality of Experience for Industrial Internet of Things2020Licentiate thesis, comprehensive summary (Other academic)
    Abstract [sv]

    Today’s research on Quality of Experience (QoE) mainly addresses multimedia services, where the end-users' subjective perception is the prime factor of determining the QoE. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as remotely-controlled operations, autonomous vehicles (AVs), and energy management, are more complex, creating additional quality requirements emerging from the machine-to-machine (M2M) communication and autonomous processes. One challenge, as an extension of the legacy QoE concept, is understanding the perception of end-users QoE in the context of IoT services. For instance, within the current state of the art in QoE it is not clear how intelligent machines can impact end-users' QoE, but also how end-users can alter or affect an intelligent machine. Another challenge is the quality evaluation of the M2M and systems that enable the machines to run by themselves. Consider a self-driving vehicle, where multiple autonomous decisions are simultaneously made as a result of predictive models that reason on the vehicle's generated data. An evaluation of the predictive models is inevitable due to abundance of the potential sources of failures. A quality degradation of the IoT hardware, the software enabling autonomous decision, and the M2M communication can raise life-threatening concerns, directly impacting the end-users' QoE. In this thesis, we argue for a paradigm shift in the QoE area that understands the relationships between humans and intelligent machines, as well as within the machines. Our contributions are as follows: first, we introduce the term Quality of IoT-experience (QoIoT) to extend the conventional QoE approaches in covering IoT services. Within QoIoT, we consider a quality evaluation from the perspective of the end-users, as well as from intelligent machines. The end-user's perception is captured by following the conventional QoE approaches, while regarding intelligent machine we propose the usage of objective metrics to describe their experiences and performance. As our second contribution, we propose a novel QoIoT architecture that consists of a layered methodology in order to determine the overall QoIoT. The QoIoT architecture, firstly, models the data-sources of an IoT service, classified within four layers: physical, network, application, and virtual. Secondly, the architecture proposes three layers for measuring the QoIoT by considering Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC), with QoC being the prime layer in measuring the objective performance metrics. Finally, the third contribution considers a case-study of cellular IoT, involving autonomous mining vehicles, which we utilize to achieve a preliminary results that validate the proposed QoIoT architecture.

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  • 3.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Modeling Quality of IoT Experience in Autonomous Vehicles2020In: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 7, no 5, p. 1-17, article id IoT-7417-2019Article in journal (Refereed)
    Abstract [en]

    Today's research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.

  • 4.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Johansson, Per
    InfoVista Sweden AB.
    Analysis and Estimation of Video QoE in Wireless Cellular Networks using Machine Learning2019In: 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    The use of video streaming services are increasing in the cellular networks, inferring a need to monitor video quality to meet users' Quality of Experience (QoE). The so-called no-reference (NR) models for estimating video quality metrics mainly rely on packet-header and bitstream information. However, there are situations where the availability of such information is limited due to tighten security and encryption, which necessitates exploration of alternative parameters for conducting video QoE assessment. In this study we collect real-live in-smartphone measurements describing the radio link of the LTE connection while streaming reference videos in uplink. The radio measurements include metrics such as RSSI, RSRP, RSRQ, and CINR. We then use these radio metrics to train a Random Forrest machine learning model against calculated video quality metrics from the reference videos. The aim is to estimate the Mean Opinion Score (MOS), PSNR, Frame delay, Frame skips, and Blurriness. Our result show 94% classification accuracy, and 85% model accuracy (R 2 value) when predicting the MOS using regression. Correspondingly, we achieve 89%, 84%, 85%, and 82% classification accuracy when predicting PSNR, Frame delay, Frame Skips, and Blurriness respectively. Further, we achieve 81%, 77%, 79%, and 75% model accuracy (R 2 value) regarding the same parameters using regression.

  • 5.
    Minovski, Dimitar
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. InfoVista Sweden AB.
    Åhlund, Christer
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mitra, Karan
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhohov, Roman
    Quality of Experience for the Internet of Things2020In: IT Professional Magazine, ISSN 1520-9202, E-ISSN 1941-045X, p. 1-9, article id ITPro-2018-09-0068Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) brings a set of unique and complex challenges to the field of Quality of Experience (QoE) evaluation. The state-of-the-art research in QoE mainly targets multimedia services, such as voice, video, and the Web, to determine quality perceived by end-users. Therein, main evaluation metrics involve subjective and objective human factors and network quality factors. Emerging IoT may also include intelligent machines within services, such as health-care, logistics, and manufacturing. The integration of new technologies such as machine-to-machine communications and artificial intelligence within IoT services may lead to service quality degradation caused by machines. In this article, we argue that evaluating QoE in the IoT services should also involve novel metrics for measuring the performance of the machines alongside metrics for end-users' QoE. This article extends the legacy QoE definition in the area of IoT and defines conceptual metrics for evaluating QoE using an industrial IoT case study.

  • 6.
    Zhohov, Roman
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Minovski, Dimitar
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. InfoVista Sweden.
    Johansson, Per
    InfoVista Sweden.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Real-time Performance Evaluation of LTE for IIoT2018In: Proceedings of the 43rd IEEE Conference on Local Computer Networks (LCN) / [ed] Soumaya Cherkaoui, Institute of Electrical and Electronics Engineers (IEEE), 2018Conference paper (Refereed)
    Abstract [en]

    Industrial Internet of Things (IIoT) is claimed to be a global booster technology for economic development. IIoT brings bulky use-cases with a simple goal of enabling automation, autonomation or just plain digitalization of industrial processes. The abundance of interconnected IoT and CPS generate additional burden on the telecommunication networks, imposing number of challenges to satisfy the key performance requirements. In particular, the QoS metrics related to real-time data exchange for critical machine-to-machine type communication. This paper analyzes a real-world example of IIoT from a QoS perspective, such as remotely operated underground mining vehicle. As part of the performance evaluation, a software tool is developed for estimating the absolute, one-way delay in end-toend transmissions. The measured metric is passed to a machine learning model for one-way delay prediction based on LTE RAN measurements using a commercially available cutting-edge software tool. The achieved results prove the possibility to predict the delay figures using machine learning model with a coefficient of determination up to 90%.

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    fulltext
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