Defining Quality of Experience for the Internet of Things

Minovski, Dimitar; Åhlund, Christer; Mitra, Karan; Zhohov, Roman

doi:10.1109/mitp.2020.2968259

Cited by 13 publications

(4 citation statements)

References 14 publications

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“…To meet these demands, autonomous electric distribution and delivery vehicles are revolutionizing the industry by reducing human labor and enabling quicker deliveries, resulting in a 25% to 35% increase in the number of successful deliveries. As automation becomes more prevalent in logistics, the challenge lies in enhancing decision-making capabilities for autonomous robot navigation, which requires complex self-optimization and selfconfiguration processes [19,71]. Autonomous robots have the potential to forecast delivery schedules for electric vehicles, leading to a significant reduction in faults and errors during the delivery process [70].…”

Section: Robot-driven Logisticsmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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Background: in recent years, automation has emerged as a hot topic, showcasing its capacity to perform tasks independently, without constant supervision. While automation has witnessed substantial growth in various sectors like engineering and medicine, the logistics industry has yet to witness an equivalent surge in research and implementation. Therefore, it becomes imperative to explore the application of automation in logistics. Methods: this article aims to provide a systematic analysis of the scientific literature concerning artificial intelligence (AI) and automation in logistics, laying the groundwork for robust and relevant advancements in the field. Results: the foundation of automation lies in cutting-edge technologies such as AI, machine learning, and deep learning, enabling self-problem resolution and autonomous task execution, reducing the reliance on human labor. Consequently, the implementation of smart logistics through automation has the potential to enhance competitiveness and minimize the margin of error. The impact of AI and robot-driven logistics on automation in logistics is profound. Through collaborative efforts in human–robot integration (HRI), there emerges an opportunity to develop social service robots that coexist harmoniously with humans. This integration can lead to a revolutionary transformation in logistics operations. By exploring the scientific literature on AI and automation in logistics, this article seeks to unravel critical insights into the practical application of automation, thus bridging the existing research gap in the logistics industry. Conclusions: the findings underscore the impact of artificial intelligence and robot-driven logistics on improving operational efficiency, reducing errors, and enhancing competitiveness. The research also provided valuable insights into the applications of various automation techniques, including machine learning and deep learning, in the logistics domain. Hence, the study’s insights can guide practitioners and decision makers in implementing effective automation strategies, thereby improving overall performance and adaptability in the dynamic logistics landscape. Understanding these foundations can pave the way for a future where automation and human expertise work hand in hand to drive logistics toward unparalleled efficiency and success.

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Section: Robot-driven Logisticsmentioning

confidence: 99%

A Systematic Literature Review on the Application of Automation in Logistics

Ferreira,

Reis

2023

Logistics

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“…Previously, impact factors of user experiences at each layer are elaborated in Table 3. However, recent concepts such as QoD, QoI and QoB (Quality of Business) in [47], [48], and [67] have emerged for evaluating QoE in the IoT context. QoD is the quality of sensed data composed of data accuracy, truthfulness, completeness, and up-to-dateness.…”

Section: A Qoe Cause Factorsmentioning

confidence: 99%

“…Unlike in the multimedia context, recent studies in the IoT domain [67] proposed a new method to measure the QoE when interaction among machines is involved. Nevertheless, interaction in the IoT context often occurs between machines or machine-to-machine (M2M) communication without user intervention.…”

Section: B Qoe Measurement and Indicatormentioning

confidence: 99%

“…Subjective tests are not even practical. As a result, Minovski et al in [67] introduced a novel term to define the quality of experience in the IoT paradigm: Quality of IoT or QoIoT. This term is utilized to model the QoE from the users' perspective, including both objective or subjective factors and machine experiences or Quality of Machine Experience (QoME).…”

Section: B Qoe Measurement and Indicatormentioning

confidence: 99%

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QoE-Driven IoT Architecture: A Comprehensive Review on System and Resource Management

et al. 2022

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Internet of Things (IoT) services have grown substantially in recent years. Consequently, IoT service providers (SPs) are emerging in the market and competing to offer their services. Many IoT applications utilize these services in an integrated manner with different Quality-of-Service (QoS) requirements. Thus, the provisioning of end-to-end QoS is getting more indispensable for IoT platforms. However, provisioning the system by using only QoS metrics without considering user experiences is not sufficient. Recently, Quality of Experience (QoE) model has become a promising approach to quantify actual user experiences of services. A holistic design approach that considers constraints of various QoS/QoE metrics together is needed to satisfy requirements of these applications and services. Besides, IoT services may operate in environments with limited resources. Therefore, effective management of services and system resources is essential for QoS/QoE support. This paper provides a comprehensive survey for the state-of-the-art studies on IoT services with QoS/QoE perspective. Our contributions are threefold: (1) QoEdriven architecture is demonstrated by classifying vital components according to QoE-related functions in prior studies, (2) QoE metrics and QoE optimization objectives are classified by corresponding system and resource control problems in the architecture, and (3) QoE-aware resource management e.g., QoE-aware offloading, placement and data caching policies with recent Machine Learning approaches are extensively reviewed. INDEX TERMSInternet of Things, Quality of Service, Quality of Experience, IoT services, IoT applications, QoS for IoT services, QoS metrics, QoE metrics, IoT architecture Recently, emerging IoT architectures with multi-layers, e.g., Mobile Edge Computing (MEC), Fog Computing and Cloud Computing, have been proposed to improve user experiences.

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