# Toward a Trust Evaluation Mechanism in the Social Internet of Things
## 3 Trust Evaluation Model: Background and Provisions
### Related Work on Trust Evaluation  
Despite the importance of trust in computer science, there are limited notable articles that clearly clarify the trust concept, trust models and evaluation mechanisms, especially in the IoT environment. A variety of models and mechanisms have been proposed for evaluating trust, however, they have mainly focused on building reputation systems in social networks for e-Commerce services [21; 22] or focused on developing trust management mechanisms in distributed systems such as WSNs [23; 24], mobile ad-hoc networks (MANET) [25; 26; 27], and P2P networks [6; 28]. The trust evaluation mechanisms in these articles are mostly based on insufficient information (i.e., only direct observation information or only third-party information). Some trust models attempt to assess trustee's trustworthiness by introducing some TAs and associated evaluation mechanisms for generating a so-called trust. They indeed calculate _direct trust_ that is a portion of the perceived trustworthiness. Researchers have pointed out that in some scenarios such as MANETs, due to high mobility, it is challenged to maintain a centralized system for managing third-party information, resulting in only direct observation information is possibly obtained; and they have to adapt the trust models based on constrains of the environments [25; 26]. In these evaluation models, the _direct trust_ consists of a set of manifold TAs that are necessary and sufficient for a trustor to quantify trust in a particular environment. The perceived trustworthiness is not required to cover all TAs, instead, the set of TAs should be deliberately chosen based on the trustor's propensity and the environmental factors (even though in these articles, the trustor's propensity and the environment characteristics are not mentioned). For example, when evaluating trustworthiness of sensor nodes in WSNs, [PERSON] and [PERSON] have used Cooperativeness, Community-Interest, and Honesty to judge whether a sensor node is malicious or not. These TAs help to evaluate trustworthiness of a sensor node in a WSN that contains some types of vulnerabilities and attacks [23]. The disadvantage of this approach is that the authors do not have a mechanism to combine such information to illustrate the subjectivity of trust. Thus, what they calculate is as an instance of entity's trustworthiness. [PERSON] et al. in [24] have analyzed various types of threats and attacks and variety of trust models in the WSN environment for secure routing protocols by characterizing many attributes of a secure system such as security mechanisms and attack preventing mechanisms. [PERSON] et al. in [27] have used only local information about a node for evaluating trust, giving an incomplete partial trust for a trust management called Objective Trust Management Framework (OTMF) in MANETs environment. The novel idea is that they apply a modified Bayesian model using different weights assigned for each information obtained from direct observations. The information is collected using a watchdog mechanism; and in order to calculate weights for each kind of information, the OTMF floods all the observation information throughout the network. A node can rely on the observation from neighbors (called second-hand information) for determining its own weights. The problem of the mechanism is the generation of a significant amount of overhead to MANETs. In [6; 29], the authors have mentioned about trust-related information extracted from the three layers of a networking system namely physical, core and application layers; and they use the information for quantifying trust. An inference engine based on fuzzy logics is used to infer a trust level. However, the drawback of this approach is only focusing on objective factors only but not subjective factors of trust. As a result, values they got from the computation mechanism do not reflect some key characteristics of trust, thus cannot quantify as trust.