# Defining and Assessing Quality in IoT Environments: A Survey
###### Abstract  
With the proliferation of multimedia services, Quality of Experience (QoE) has gained a lot of attention. QoE ties together the users' needs and expectations to multimedia application and network performance. However, in various Internet of Things (IoT) applications such as healthcare, surveillance systems, traffic monitoring, etc., human feedback can be limited or infeasible. Moreover, for immersive augmented and virtual reality, as well as other mulsemedia applications, the evaluation in terms of quality cannot only focus on the sight and hearing senses. Therefore, the traditional QoE definition and approaches for evaluating multimedia services might not be suitable for the IoT paradigm, and more quality metrics are required in order to evaluate the quality in IoT. In this paper, we review existing quality definitions, quality influence factors (IFs) and assessment approaches for IoT. This paper also introduces challenges in the area of quality assessment for the IoT paradigm.  
QoS; QoE; IoT; QoD; QoI; mulsemedia; fog computing; QoE influence factors +
Footnote †: journal: 2022  
definition  
Currently, there is no standardization or set of best practices as to how the subjective tests can be conducted and even if there was, it would be practically infeasible to carry out subjective tests for every existing as well as new applications [7]. Moreover, existing subjective methodologies do not consider QoE influence factors (IFs) of the IoT environment, such as the usefulness of the application [7].  
Thus, in such heterogeneous environments, existing quality related issues that were initially tailored for humans, such as QoE definitions, evaluation approaches and provision mechanisms, should be re-examined in order to check their validity in a machine-to machine environment. In addition, in cases where human interaction is not required, the traditional definition of QoE is not valid, and new metrics to evaluate the quality in IoT environments are required.  
In the literature, several survey papers may be found that focus on IoT, e.g., [10, 11] or QoE, e.g., [12, 13, 14]). However, there are few survey papers that examine QoE in IoT environments.  
[PERSON] et al. [15] reviewed existing QoE definitions and QoE models for autonomic IoT. However, the authors suggested only one definition for QoE in IoT. In addition, regarding the QoE modeling, the authors provided limited information concerning the role of data in the QoE evaluation of the IoT applications. Moreover, the authors in [7] focused on the QoE IFs and presented a QoE taxonomy for IoT, while [PERSON] et al. [16] consolidated the IoT quality characteristics into a unified view. A survey concerning QoE evaluation for autonomic IoT applications can be found in [17].  
The contributions of the current paper are as follows:  
* Review existing definitions of QoE that are suitable for IoT environments, since nowadays new terms have been introduced to define and evaluate the quality of IoT applications.
* Identify and categorize the quality IFs for IoT. More specifically, we have collected and classified IFs that may found into the literature and are necessary for the creation of a successful quality model for IoT.
* Review existing quality assessment approaches for IoT applications.  
The rest of this paper is organized as follows: Section 1 presents an overview of IoT and fog computing architectures. Section 2 reviews existing quality definitions that are suitable for IoT, while Section 3 overviews and categorizes quality indicators for IoT. Section 4 reviews existing QoE models and frameworks for IoT. Section 5 discusses challenges in the area of QoE assessment in the IoT context, while Section 6 concludes the paper.