# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 4 Use Cases
### Semantic Annotation and Reasoning
[PERSON]'s perceived temperature is estimated according to the user environment. For example, it may be estimated at home with no heating devices. However, in the laboratory, it is necessary to consider the impacts from the working computers, servers and laboratory area to estimate the raised temperature's amplitude. These impacts' value can be acquired by the sensors attached to the locations, where people always stay. These sensors will upload the temperature information to the smart gate way for further analysis. Then, the appropriate cooling temperature can be set by comparison with the results at [PERSON]'s home. If there is more than one person in the laboratory, the average body perceived temperature could be used to describe all users' preferences. When one of them has got a cold, the disease would be incorporated into the user model as a semantic annotation. The suitable temperature of the sick person would be given priority. Figure 11: The construction of user model (**a**) Independent model (**b**) Shared model. In addition, we analyze the effects on users' QoE with the independent user model and the shared user model, which are shown in Figure 11. The independent model (IM) only contains the information of one user activity area, but the shared model (SM) can contain the user information of all the related activity areas. The model of user's QoE can be constructed according to the user's feelings, e.g., the indoor temperature is hot or cold [57]. According to our measured data, we can construct the temperature model in a room as:  
\[T(t)=(T_{0}-T_{\rm c})\exp\left(-\frac{\alpha\cdot W}{\beta\cdot S}\times t \right)+T_{\rm c} \tag{1}\]  
where \(T_{0}\) is the current indoor temperature, \(T_{\rm c}\) is the cooling set temperature, \(W\) represents the wind speed of air conditioner, \(S\) represents the floor space, the \(\alpha\) and \(\beta\) are the tuning parameters, and \(t\) represents the time. The item \(\frac{\alpha\cdot W}{\beta\cdot S}\) is the impact factor (IF). When IF = 1, the temperature variation curve is shown in Figure 12. The starting point represents the beginning of cooling and the indoor temperature is 35 \({}^{\circ}\)C before the starting point. The tolerable critical point represents the time when the temperature is in the acceptable range, which is defined as \(T_{\rm c}\pm 1\)\({}^{\circ}\)C. In this paper we set \(T_{\rm c}\) as 25 \({}^{\circ}\)C, which is the ideal temperature value. The tolerable temperature is set as 26 \({}^{\circ}\)C. Figure 13 shows the respective temperature variation curves with IF = 0.5, 1 and 2, respectively, when the cooling is on. We can note that when the IF increases (i.e., the \(W\) increases and the \(S\) decreases), the cooling effect becomes more prominent. Figure 12: The indoor temperature variation when cooling. Figure 13: The indoor temperature variation with different IF value. According to the temperature model, we can further establish the QoE model. The QoE represents the satisfaction with the service. Therefore, the differences between the current indoor temperature and the user's tolerable temperature can indicate the user's QoE. Then, we can define \(f(t)=T(t)-(T_{\rm c}+1)\), and the QoE model is defined as follows:  
Figure 10.0  
where \(t_{0}\) represents the time of the starting point and \(t_{a}\) represents the time of the tolerable critical point. Equation (2) contains the duration before the starting point because of the user's manual operation time. When the user model is finished, the QoE model can be defined as:  
Figure 10.1  
We measure the QoE in four different rooms with different user models, and the detailed measurement parameters are shown in Figure 10.2 T represents the initial temperature and the status represents whether the user triggers the air conditioner. In our experiments, we assume that the user model about the temperature preference should be confirmed, when the trigger frequency of the air conditioner is more than 3 (training critical value).