# Internet-of-Things and Information Fusion: Trust Perspective Survey
## 7 Taxonomy of Trust Models  
Based on our extensive survey of IoT trust models, we classify trust models into the taxonomy represented in Figure 2. This taxonomy classifies trust models based on five dimensions. The first dimension in our taxonomy is what are the trust components used to evaluate trust. A node can rely on its own experience or on recommendations to evaluate the trust levels of other nodes. The second dimension is to know which trust attributes are used and whether these attributes are associated with the node itself or the network. The third and fourth dimensions are concerned with how trust is discovered and computed in the trust model. Trust discovery can be centralized or distributed whereas different aggregation and customization methods can be used to compute the trust level. The final dimension answers the question of when trust levels are disseminated or advertised. Before sensor \(x\) engages in any behavior trust relationships with target sensor \(y\), \(x\) needs to evaluate the trust level of \(y\) which we will refer to as \(TL_{y}\). Sensor \(x\) can rely on its own experience with \(y\) or the reputation of \(y\). Therefore, trust has two components namely, direct trust and reputation. It should be noted that \(x\) can use both of these components to form \(TL_{y}\). Sensor \(x\) needs also to determine the quantity of trust attributes (either a single attribute or multiple attributes) as well as whether these are network or node attributes. For example, \(x\) can use two attributes such as honesty and packet delivery ratio to determine \(TL_{y}\). In this case, honesty pertains to node \(y\) itself but packet delivery ratio pertains to the network. Node attributes can be QoS or social attributes whereas network attributes are only QoS-related attributes. Trust discovery is concerned with trust revelation schemes. In general, the nodes that store and reveal trust levels are either distributed or centralized. A centralized trust discovery scheme requires a centralized node that can be represented by either a physical or a virtual node. This centralized node stores and/or reveals trust levels. Distributed trust discovery refers to having multiple nodes storing and/or revealing trust levels. This means that (a) trust levels are stored either on a centralized or distributed node(s) and (b) a centralized or distributed node(s) can reveal trust levels. In a nutshell, trust discovery is concerned with the nodes that store and reveal trust levels. On the other hand, trust advertisement is concerned with when should a node reveal trust levels. Trust advertisement is initiated by a node to propagate trust levels to other nodes. In a periodic \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Architecture Type**} \\ \cline{3-5}  & & **Centralized** & **Distributed** & **Hybrid** \\ \hline \multirow{3}{*}{IoT Layer} & Things & [63; 64] & [68; 69; 70] & \\  & Fog & [36] & [7] & [44] \\ \cline{1-1}  & Cloud & [33; 61; 65; 75] & & \\ \cline{1-1}  & & [20] & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of existing IoT architectures for modelling trust. Figure 2: Taxonomy of Internet-of-Things Trust Models. approach, trust levels are periodically disseminated from the originating node to other node(s) in which case it is pushing the trust levels. On the other hand, other nodes can request the trust levels in which case they are pulling the trust levels from the originating node. Trust levels can also be advertised based on events. For example, after every transaction, the trust level is pushed from the originating node to other nodes. In an on-demand approach, trust levels are advertised from the originating node immediately once it is demanded (requested) by other nodes. In this case, the information is pulled by the other nodes. Trust models can be classified also based on the trust computation phase. That is, how the target sensor's final trust level is computed. Once all the trust levels are collected, they need to be combined.