# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 8 Internet-of-Things Trust Models
### QoS-Social-Driven Trust Models  
According to Refs. [68; 76; 77] trust assessment, evaluation and management depend on interactions among objects in social networks resulting in a distributed, encounter-based computations. Trust in such models is extracted based on social perceptions such as recommendation, reputation, and involvement by spreading knowledge between objects. The key disadvantage of these reputation methods is the need of human involvement for inputs. Together with the online transactions, the reputation methods can be used in MANETs, WSNs, and P2P systems that utilize interactions among objects that are spread over a network. For instance, various trust-based routing protocols in MANETS and WSNs evaluate the trustworthiness of a machine in the network by taking into account third-party thoughts and reputation besides their own knowledge to assure that a machine or a node will not get compromised. In the literature, SIoT metrics have been used in trust computation phase to effectively manage trust in IoT environments. In [68], authors proposed a first distributed trust management model based on social relationships between owners of IoT devices. They consider three trust metrics: honesty based on node's experiences, cooperativeness that is identified by the number of common friends, and community-based on the degree of common interest or similar capabilities. [PERSON] et al. [78] presented a new approach for social network intelligent objects derived from SIoT based on subjective trust management model. Analogous to social networks for people, they defined a social network of intelligent objects that are linked with social relationships between objects. Their research work was inspired by trust in P2P networks. The bottom line of trust calculation depends mainly on a node's knowledge and reputation among its known friends. Additionally,they developed a feedback system in which they combine the importance of the participated nodes and the trustworthiness. Furthermore, authors in [77] proposed a subjective trustworthiness evaluation model in SIoT environments. The computation of trust is performed at each node based on direct trust such as its own experiences and indirect trust such as the recommendation of other common friends towards a service provider. In the trust evaluation, they consider centrality as a measure of node willingness, node capabilities as a measure of context information, transaction relevance and other social relationships. [PERSON] et al. [79] proposed a hybrid trust model for SIoT applications. Both QoS and social trust attributes are considered in this model. QoS trust attributes include computational capability and transaction service quality, while social trust attributes include credibility, centrality, and relationship factors. In this model, they consider features from both distributed subjective and centralized objective trust models. In the distributed model, each node computes its own subjective trustworthiness towards another node based on its own experience and on the recommendations of its friends. In the centralized model, they proposed to propagate the trust assessments to centralized pre-trusted objects that maintain a distributed hash table to answer trust related inquires for a service provider. They assumed that pre-trusted objects are dedicated to answering inquiries and will not offer any services themselves. Another adaptive distributed trust model is proposed by [PERSON] et al. [80] to enhance cooperation between trusted nodes and adjust the trust scores dynamically based on the node behavior. In this model, node transaction attributes (e.g., node computation power, confidence, context importance, and feedback), and node social attributes(e.g., friendship, centrality, and relationship) are considered. In the trust computation phase, each node computes the overall trust values of other nodes based on its own direct interactions and recommendations from other nodes.