Towards Quality of Experience for Industrial Internet of Things
Abstract: 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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