Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Park, Jinho; Chung, Kwangsue

doi:10.3390/s21248500

Cited by 2 publications

(3 citation statements)

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“…Thus, real user experiences are not captured by existing offloading policies. Recent QoE-related offloading schemes are listed in [127], [128], [80], [138], [139], [140], [79], [129], [130], [141]. The summary of these works is shown in Table 8.…”

Section: A Qos/qoe and Computational Offloadingmentioning

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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Section: A Qos/qoe and Computational Offloadingmentioning

confidence: 99%

“…Also, as shown in [139], Pham et al applied Branch and Bound techniques to determine the offloading algorithm maximizing the QoE utility function. Greedy-based heuristic algorithm is used in [140] to improve the QoE by minimizing the overall completion time of the system.…”

Section: A Qos/qoe and Computational Offloadingmentioning

confidence: 99%

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

et al. 2022

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“…These metrics are still in use, but other emerging metrics supplement them. Quality of user Experience (QoE) was first adopted as a natural evolution of QoS [7][8][9], incorporating new measurements to get a complete overall vision of systems' performance. Such is also the case of the quality model proposed by the authors in ref.…”

Section: Introductionmentioning

confidence: 99%

A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

Martinez‐Caro,

Tasic,

Cano

2023

IET Wireless Sensor Systems

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Communication architectures based on the Internet of Things (IoT) are increasingly frequent. Commonly, these solutions are used to carry out control and monitoring activities. It is easy to find cases for manufacturing, prediction maintenance, Smart Cities, etc., where sensors are deployed to capture data that is sent to the cloud through edge devices or gateways. Then that data is processed to provide useful information and perform additional actions if required. As crucial as deploying these monitoring solutions is to verify their operation. In this article, we propose a novel warning method to monitor the performance of IoT‐based systems. The proposal is based on a holistic quality model called Quality of X (QoX). QoX refers to the use of a variety of metrics to measure system performance at different quality dimensions. These quality dimensions are data (Quality of Data, QoD), information (Quality of Information, QoI), users' experience (Quality of user Experience, QoE), and cost (Quality Cost, QC). In addition to showing the IoT system performance in terms of QoX in real‐time, our proposal includes (i) a forecasting model for independent estimation of QoX applying Deep Learning (DL), specifically using a Long Short‐Term Memory (LSTM) and time series, and (ii) the warning system. In light of our results, our proposal shows a better capacity to forecast quality drops in the IoT‐based monitoring system than other solutions from the related literature.

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Resource Prediction-Based Edge Collaboration Scheme for Improving QoE

Cited by 2 publications

References 35 publications

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

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

A novel system to control and forecast QoX performance in IoT‐based monitoring platforms

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