Cited by 127IoT and Edge/Fog ComputingImage and Video Quality AssessmentInternet of Things and AI

The DOI matches its Crossref record; 49 of the 50 references it cites were found in a registry or an index; 13 of the 55 sources its claims cite were checked for support.

QoE in IoT: a vision, survey and future directions

Open access

Abstract

The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decision-making (actuation) process. To ensure that such IoT applications (we term them autonomic) function as expected, it is necessary to measure and evaluate their quality, which is challenging in the absence of any human involvement or feedback. Existing Quality of Experience (QoE) literature and most QoE definitions focuses on evaluating application quality from the lens of human receiving application services. However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses. In this paper, we present a vision, survey and future directions for QoE research in IoT. We review existing QoE definitions followed by a survey of techniques and approaches in the literature used to evaluate QoE in IoT. We identify and review the role of data from the perspective of IoT architectures, which is a critical factor when evaluating the QoE of IoT applications. We conclude the paper by identifying and presenting our vision for future research in evaluating the QoE of autonomic IoT applications.

Overview

AI snapshot

Written from the paper’s full text.

TL;DR

This review surveys QoE definitions and models for IoT, arguing that existing QoE is human-centric and insufficient for autonomic IoT applications where devices sense, analyze, and actuate without human feedback. It presents a vision and future directions for objective, data-aware QoE evaluation across the IoT application lifecycle.

Key findings

  • Existing QoE definitions from ITU-T, ETSI, Qualinet, Nokia, Ericsson, and others focus on end-user subjective perception; the paper states there is no widely generic QoE definition for IoT, and only Minovski et al.’s QoIoT/QoME work addresses machine-focused quality alongside user-centric QoE.
  • In the surveyed literature, 77.77% of work involves human subjective evaluation for QoE in IoT, while 22.22% involves no human feedback for evaluation.
  • Reviewed QoE models either decompose application QoE into submetrics such as Quality of Data (QoD), Quality of Information (QoI), and QoS, or evaluate QoE without decomposition using subjective user feedback and IoT architectural layers; one QoD/QoI-based model reported a Pearson correlation of 0.81 with subjective test results.
  • Data quality is identified as crucial for IoT application quality, characterized by metrics including accuracy, completeness, usefulness, and timeliness.

Methods

  • Survey of research published from 2007 to the present (2020) referring to QoE in IoT, using Google Scholar, Springer, Science Direct, and IEEE Xplore, with search terms including “Quality of experience” and “IoT”, “QoE definition” and “IoT”, “QoE” and “data”, and “QoE models” in Abstract/Title/Keywords.
  • The paper reports selecting 50 papers in total, while also stating that 53 papers were surveyed; papers were classified by reading abstracts and full papers when necessary, with duplicates across databases eliminated.
  • Narrative review and synthesis of QoE definitions, QoE models, metrics, datasets, and the role of data; no primary empirical experiment is conducted.

Limitations

  • The paper states that no widely generic QoE definition exists for IoT, and existing definitions rely on human feedback and do not cover sensing, analytics, and actuation or machine experiences.
  • The paper notes a lack of general IoT architecture in the literature, and reviewed QoE models often use synthetic data or human feedback, limiting their applicability to autonomic IoT applications.
  • Evident from the text: the selection criteria list appears incomplete after “were the following,” the survey count is inconsistent (50 vs. 53 papers), and many reviewed works are summarized without detailed extraction.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1007/s43926-021-00006-7

Cites

49 of 49 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

29 No full text of the sources · 24 Own interpretation · 18 No basis given · 6 Partly supported · 5 Own result · 4 Supported · 3 Sources not found · 3 Own method · 2 Supported so far · 1 Not supported · 1 Not checked yet · accuracy not yet measured

Show all 32 claims

  • Probably not a substantial claim. 54
  • It may state two separate claims as one. 26
  • The quote only partly states this claim. 1
64 of 64
  • The paper relies on this without support.

    In autonomic IoT applications, poor quality of decisions and resulting actions can degrade application quality, leading to economic and social losses.

    “However, in autonomic IoT applications, poor quality of decisions and resulting actions can degrade the application quality leading to economic and social losses.”
  • The paper relies on this without support.

    Manufacturing KPIs are typically not measured during production.

    “Firstly, there is typically no human feedback available based on which the QoE of the application can be estimated in real-time, since the manufacturing KPIs are typically not measured during production.”
  • The paper relies on this without support.

    IoT applications increasingly require minimal or no human involvement in data collection, transformation, knowledge extraction, and decision-making (actuation).

    “The rapid evolution of the Internet of Things (IoT) is making way for the development of several IoT applications that require minimal or no human involvement in the data collection, transformation, knowledge extraction, and decisionmaking (actuation) process.”
  • The paper relies on this without support.

    IoT applications are often systems of systems, in which multiple autonomous IoT applications interact with each other.

    “IoT applications are often systems of systems, in which multiple autonomous IoT applications interact with each other.”
  • The paper relies on this without support.

    IoT applications are inherently distributed, and this distribution results from both computation requirements and the distinct roles of application components that contribute differently to the application outcome.

    “IoT applications are distinct from other applications in the fact that not only are they inherently distributed, but the distribution is result of both computation requirements and the distinct roles of application components that contribute differently to the application outcome.”
  • The paper relies on this without support.

    The 53 surveyed papers are representative of the literature on QoE in IoT.

    “The plethora of the paper we have surveyed (53 papers) have considered human involvement in QoE evaluation for IoT as shown in Fig. 3.”
  • The paper relies on this without support.

    It is possible to evaluate application quality without human feedback by identifying factors that impact it.

    “Suppose the designer of the application can identify the factors and evaluate their impact on the application quality.In that case, such QoE models can be used for applications where a human is not present to provide his subjective feedback.”
7 of 7

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

4 of 4

Citations

Cites

Research works printed but not linked: 2

  1. Ericsson. Video QoE: leveraging standards to meet rising user expectations; 2017. https://www.ericsson.com/en/reports-and-papers/ericsson-technology-review/articles/video-qoe-leveraging-standards-to-meet-rising-user-expectations. Accessed 12 Dec 2020.
  2. Floris A, Atzori L. Towards the evaluation of quality of experience of internet of things applications. IEEE Internet Things J. 2017.

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Metadata

Authors
Fizza, K., Banerjee, A., Mitra, K., Jayaraman, P. P., Ranjan, R., Patel, P., & Georgakopoulos, D.
Affiliations
  • Swinburne University of Technology (AU)
  • Luleå University of Technology (SE)
  • Newcastle University (GB)
  • Ollscoil na Gaillimhe – University of Galway (IE)
Corresponding author
  • Fizza, Kaneez
  • Jayaraman, Prem Prakash
  • Ranjan, Rajiv
Venue
Discover Internet of Things
Published
2021-02-24
Volume
1
Issue
1
ISSN
2730-7239 (ISSN-L)
OpenAlex
W3164531790
Cited by
127
References
50
Citations by year
2026: 17 · 2025: 14 · 2024: 21 · 2023: 30 · 2022: 24 · 2021: 21
FWCI
9.60
Type
journal article
Language
en
Open access
Locations
  • Discover Internet of Things (publishedVersion, cc-by)
  • KTH Publication Database DiVA (KTH Royal Institute of Technology) (submittedVersion, other-oa)
  • Newcastle University ePrints (Newcastle Univesity) (cc-by)
Funders
  • National Natural Science Foundation of China (2019-2021, 2019-2022)
  • Engineering and Physical Sciences Research Council (EP/T021985/1)
  • Research England
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › IoT and Edge/Fog Computing
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
  • Physical Sciences › Computer Science › Information Systems › Internet of Things and AI
Keywords
quality of experience, Internet of Things, QoE evaluation, IoT architecture, decision quality, actuation

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