# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 4 REK Trust Evaluation Model in the SIoT
### Aggregation Mechanism for REK Trust Evaluation Model  
The outcome of the REK Trust Evaluation model is aggregated based on the triad Reputation, Experience, and Knowledge TIs. It also requires to aggregate TAs to derive Knowledge TI. As clarified in the conceptual trust model as well as the REK model, these aggregations should take both environmental factors and trustor's propensity into consideration. Technically, there are two common approaches to attain TIs from associated attributes; and to finalize an overall trust value from the three TIs. The choice between the two depends on specific scenarios such as information modelling of TAs, of the trustor's preferences, and of the environmental factors. The first approach is to use mathematical models such as weighted sum [61; 62], Bayesian neutron networks [63; 64], and machine learning algorithms such as linear regression [65]. These models use mathematical models to express trustor's propensity and environment conditions by assigning weights for individual features (i.e., TAs and TIs). These values can be autonomously updated depending on outcomes of the models by using a feedback mechanism. The second method makes use of an inference engine for inferring new knowledge from a knowledge-base such as reasoning mechanisms [66] and fuzzy-based mechanisms [18; 67]. These inferring mechanisms are frequently used for deriving causal-consequence knowledge that is also appropriate for incorporating trustor's propensity and environmental factors. In the second approach, all trust-related information already obtained (e.g., TAs, Experience TI, and Reputation TI) are represented in form of facts; trustor's propensity and environmental factors are represented in form of logics applied upon the facts (e.g., rules in reasoning mechanisms, and membership functions in fuzzy-based mechanisms). Based on the set of logic, an inference engine can draw new knowledge that is being interested such as Knowledge TI and the overall trust value. In real implementation, a set of default logics should be already investigated and deployed for all entities. Then a trustor might have more preferences or a considering environment might have different conditions; then these factors are converted into logics that replace or supplement the default set of logics. For example, we have used the Apache Jena framework in the trust demonstration for the User Recruitment in MCS use-case which integrates several types of inference engines including the generic rule-based reasoner that enables predefined rules. Before that, all TAs, Reputation TI, and Experience TI already obtained are converted into semantic information as metadata in forms of facts in Description Logics [68] represented in RDFS/OWL languages (Figure 10). The Jena integrated rule-based reasoner supports both forward chaining and tabled backward chaining reasoning strategies as well as the hybrid approach. For example, a generic hybrid reasoner in the Jena framework is used in the demonstration to infer reputation values and experience values in form of levels (i.e., _low_, _medium_, and _high_) from the actual calculated values (the calculated values are in the range [0-1] and obtained using the proposed Experience TI model and Reputation TI model); as well as to infer the level of trust which is the overall trust value we are interested. In the User Recruitment in MCS demonstration, values of TAs such as Spatial Distance, Availability, Dependability, Cooperativeness and Honesty are already obtained and then represented in form of facts in the trust knowledge-based. Trustor's propensity is represented in form of rules upon literals introduced in the facts. For example, with a same trustee with calculated reputation value is 0.45; a trustor could consider that Reputation TI is _low_ but another trustor considers Reputation TI as _medium_.