# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 4 REK Trust Evaluation Model in the SIoT
### Reputation TI Evaluation Model  
Reputation is a social concept which corresponds to what is generally understood about entity's characteristics. Reputation of any entity should be public and is determined by aggregating opinions of other in its social groups. Reputation has been intensively carried out in both computer sciences and information sciences recent years [7; 52; 53; 54]. A reputation system is frequently found in e-Commerce websites for encouraging online transactions by providing evidences of trust to help people interact with each other without having firsthand knowledge. Thus, in this case, reputation can serve as a basic for trust. Reputation systems are mostly based on feedback from the participants in the transactions (as the trustors) about how a trustee has accomplished a given task (the trust goal), in both positive and negative opinions. This feedback is then aggregated and presented to the public as an estimate of the trustee' trustworthiness. Therefore, a reputation mechanism is necessary for managing feedback as well as for evaluating, propagating, and maintaining reputation values for each entity in SIoT. For instance, eBay, IMDb and Keynote use a centralized trust authority to establish and maintain user ratings whereas Google has developed a distributed approach for assessing reputation of webpages based on backlinks. They use several heuristic algorithms for reputation integration and update on evaluation process. In the scenarios of the SIoT environment, as mentioned in Section 4.3, feedback is a form of outcomes of interactions; and Experience TI is considered as an aggregation of feedback from a specific entity to another. Experience TI model shows that each of entities (as the trustor) which has previous interactions with a specific entity (as the trustee) holds an opinion about the trustee as its experiences. And if all of these entities share their opinions as recommendations about the trustee to others, we can come up with a model that aggregates these recommendations to form a unique value about the trustee as the trustee's reputation. A necessary consideration is that each of the recommendations contributes differently to the trustee's reputation. The weight a trustor's recommendation contributing to the trustee's reputation depends on both Experience TI (between the trustor and the trustee) and Reputation of the trustor itself. Therefore, appropriate reputation models should not only take the experience values into account but also the reputation values of the trustors (The reputation conceptual model is illustrated in Figure 9. It is reasonable because obviously, an entity with high reputation contributes more than an entity with lower reputation to the trustee's reputation. We have come up with a non-bias mechanism for calculating recommendation and reputation values of trust for all entities in a distributed network in [55]. The mechanism, however, is conducted in the centralized authority and it requires to aggregate necessary information about the social relationships of both trustors and the trustee. In this article, inspired by the PageRankTM idea in [8], we have proposed a novel approach to calculate reputation values for entities over the SloT networks. Two challenges appeared when designing a model for the Reputation TI based on the PageRankTM algorithm: (i) Different weights of recommendations from many entities to a particular entity; and (ii) Recommendations could be both positive and negative; positive recommendations occur when Experience value \(Exp(i,N)>\theta\) result in increasing reputation of the target entity N whereas negative recommendations (\(Exp(j,N)<\theta\)) should reduce reputation. \(\theta\) is the threshold parameter indicating whether an Experience is considered as negative or positive. The original PageRankTM considers same weights for all links from a webpage to another and the mathematical model correctly works for only positive links' values (the weights for all links are assigned as 1/N where N is the total number of webpages in a network).