# Defining and Assessing Quality in IoT Environments: A Survey
## 5 Quality Models for IoTs  
Traditionally, qualitative methods that focus on voice perceptibility for applications usability are used for the QoE evaluation [39]. The QoE for multimedia services is evaluated by subjective, objective and hybrid assessment (a combination of both the subjective and objective approaches) [40]. In this context, a few studies that focus on modeling the relationship between human experience and quality perception in relation to the smart-wearable segment may be found in the literature [6, 41, 42]. QoE is considered a very important aspect of multiple sensorial media (mulsemedia) [43]. [PERSON] et al. [41] examined the relation of users' experience and the quality perception in IoT. To achieve this goal the authors utilized a combination of qualitative and quantitative methods. Figure 6 shows the proposed QoE model in which, besides the user's behavior, coolness, satisfaction and affordance are considered as QoE factors in the IoT context. [PERSON] et al. [44] proposed a QoE model that maps QoD and QoI to QoE. More specifically, the authors, in order to create the model, collect data from 5 wearable devices. Half of the data set is used to build the model, while the other half is used to test accuracy. The step-counts and heart-rate measurement readings by the wearables are used as QoD parameters, whereas the perceived ease of use, perceived usefulness, and richness in information are used as QoI parameters. The accuracy of their model is evaluated by comparing the QoE  
Figure 6: QoE IFs in the IoT context [7]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Quality IF** & **Description** & **Refs.** \\ \hline Quality of Experience (QoE)/ & Evaluates the overall acceptability of & \\ Human feedback & an application or service or system as & [7, 15, 30, 38] \\  & perceived subjectively by users & \\ Quality of Context (QoC) & Evaluates the context of the & \\  & environment or the application & [7, 17, 23] \\  & Evaluates the cost in terms of of & \\ Quality of Cost (QoCo) & computation, storage, or energy of an & [9] \\  & IoT application & \\ Quality of Information (QoI) & Evaluates the quality of information & [9, 15, 17, 19, 34] \\ Quality of Data (QoD) & Evaluates the quality of data & [15, 17, 18, 19] \\ Quality of Service (QoS) & Evaluates the network’s capability to & [15, 17, 23, 33, 38] \\ Quality of Device (QoDe) & Evaluates the quality of the physical & [17] \\  & IoT devices & \\ Quality of Actuation (QoA) & Evaluates the correctness of the & \\  & decision making/ actuation & [17] \\ Quality of Security and Privacy & performed by an IoT application & \\  & Evaluates the security and privacy of & \\  & an IoT application & [17] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Quality IFs for IoT. obtained from the mathematical model and a subjective test with 40 participants. The authors adopted the Mean Opinion Score (MOS) to quantify the user experience. [PERSON] et al. [6] studied the impact that human factors such as gender, age, prior computing experience, airflow intensity and smell sensitivity have on 360\({}^{\circ}\) mulsemedia QoE. A total of 48 participants (27 male, 21 female) participated in this study with ages between 16 and 65 years old. Results showed that all these factors influence the users' QoE. Guidelines for evaluating wearables' quality of experience in a mulsemedia context can be found in [43]. In addition to the QoE evaluation for wearables, several attempts have been made in order to create QoE models for IoT. One of the first attempts was made by [PERSON] et al. [45] that calculated the overall QoE by combining two parameters: profit (expressed in terms of QoD, QoI, QoE) and cost (expressed in terms of resource efficiency, i.e., device utilization efficiency, computational efficiency, energy efficiency, storage efficiency).