# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 4 REK Trust Evaluation Model in the SIoT
### Experience TI Evaluation Model  
Experience is a social concept that represents personal understandings and opinions about one entity to another based on its previous interactions to the counterpart. We propose a conceptual model for the Experience TI depicted in Figure 8 which computes experiences based on the three factors: the current value of Experience, the outcomes, and the timestamps of individual interactions. Therefore, an outcome evaluation scheme for the interactions is one of the important components in the Experience TI model. Various mechanisms can be used to deduce outcomes of interactions depending on particular scenarios. For instance, outcomes might be feedback (in both implicit and explicit forms) from consumers after each interaction (as used in many e-Commerce and reputation systems), might just be a Boolean value (or 0/1) generated by using an ACK message to track whether the interaction has successfully accomplished or not (as in some reputation-based trust systems). For example, in Wireless Sensor Networks, interactions are package transmissions between two nodes, if a transmission is successful, then the outcome of the interaction is 1, and 0 otherwise. In a file-sharing P2P networks, interactions are file transfer transactions. If a file is successfully transferred, then the outcome of the interaction is 1; otherwise is 0. The interaction is also in form of any types of relationship between two entities. For example, Google PageRankTM considers a hyperlink as an interaction between a source webpage and a destination webpage; and the outcome value is set as 1 [8]. Figure 8: The experience TI model in the REK trust evaluation. Another important component is an aggregation model for calculating Experience TI. There is an important assumption about experience relationship between humans in sociological environment: Experience accumulates for cooperative interactions and is decreased by uncooperative interactions. It also tends to decay over time if it is not maintained by interactions. This assumption has been reasonably proven in many trust-related sociological literatures [50; 51]. Thus, there are three trends of the experience relationship: Increase, Decrease, and Decay; and all of them are measured based on three features: _intensity of interactions, values of the interactions_, as well as _the current value of the experience_. Therefore, a mathematical linear difference equation could be used to model the trends of the Experience TI. We have proposed an Experience TI model in which an outcome of an interaction is either 0 (indicates uncooperative interaction) or 1 (indicates the cooperative interaction). The model consists of three trends is proposed as following:  
* _Experience Increase (in case of a cooperative interaction occurs)_: The Experience Increase trend is modelled using a linear difference equation as following: \[Experience_{t+1}=Experience_{t}+\Delta Experience_{t+1}\] (4) \[\text{where }\Delta Experience_{t+1}=\alpha-\frac{\alpha}{max_{ Experience}}\times Experience_{t}\] (5) where \(Experience_{t}\) indicates Experience TI at the time \(t\); and \(\Delta Experience_{t}\) indicates the increase value of Experience TI. \(\alpha\) is a parameter indicating the _maximum increase value_ of the experience. \(max_{Experience}\) is a parameter indicating the _maximum value_ of Experience TI (obviously \(\alpha<max_{Experience}\)). Usually it is more convenient for Experience TI to use the same scale with trust (i.e., the range of [0,1]), thus, \(max_{Experience}\) is 1. Consequently, the Equations (4) and (5) can be rewritten as: \[Experience_{t+1}=Experience_{t}+\alpha\times(1-Experience_{t})\] (6) \[\text{or }Experience_{t+1}=(1-\alpha)\times Experience_{t}+\alpha\] (7) As shown in the Equation (6), the _increase value_\(\Delta Experience_{t+1}=\alpha\times(1-Experience_{t})\) is relatively large when the current value \(Experience_{t}\) is small; but the _increase value_ is reaching to 0 when the current value \(Experience_{t}\) is high (approaching to 1).