## 3 Key Technologies in Trust Management for WSNs
### Trust Evaluation Methods
#### Dempster-Shafer  
Each node possesses both direct and indirect trust values for its neighbor nodes. When a node shares its trust value of a specific neighbor with other nodes, the question arises whether it should share the final combined trust value, which includes both direct and indirect trust, or only the direct trust value. In the studies we reviewed, only [PERSON] et al. [5; 38] analyzed this issue and noted that to avoid the same trust information circulating back to its origin point, nodes should broadcast only their direct trust values to their neighbors. They applied the Dempster-Shafer belief theory to resolve the integration of direct and indirect trust metrics. The closed-form expression for the new trust metric after trust integration is shown in Equations (8) and (10):  
\[\alpha_{j}^{new}=\alpha_{j}+\frac{2\kappa_{k}\alpha_{j}^{k}}{\left[\left( \beta_{k}+2\right)\left(\alpha_{j}^{k}+\beta_{j}^{k}+2\right)\right]+2\alpha_{ k}} \tag{9}\]  
\[  
In the models we reviewed, except for RFSN, all the models that include indirect trust directly propagate the calculated trust values during the indirect trust propagation process, rather than propagating trust metrics. These models generally use the arithmetic mean method to integrate the trust values provided by all recommenders. Then, the direct trust value is weighted and averaged with the integrated indirect trust value [15; 16; 24; 31; 35; 42], as shown in Equations (11) and (12):  
\[IT=\frac{1}{k}\sum_{m=1}^{k}DT(m) \tag{11}\] \begin{table}
\begin{tabular}{p{85.4 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Trust Models** & **Methods** & **Comparison** \\ \hline
[5; 38] & Dempster–Shafer belief theory method & Suitable for trust models that propagate trust metrics; has strong theoretical support \\ \hline
[15; 16; 24; 31; 42] & Arithmetic mean method & Suitable for trust models that propagate trust values and do not consider the trustworthiness of the recommendation information; accuracy is low \\ \hline
[14; 17; 18; 32] & Weighted average method & Suitable for trust models that propagate trust values and consider the trustworthiness of the recommendation information based accuracy is moderate \\ \hline
[10; 30; 34; 45] & Outlier detection & Suitable for trust models that propagate trust values, using outlier detection methods to evaluate the trustworthiness of the recommendation information; accuracy is high \\ \hline
[39] & Forgetting curve method & Suitable for trust models that propagate trust values and do not consider the trustworthiness of the recommendation information; accuracy is low \\ \hline
[46] & Intrusion detection system & Uses the IDS to detect network anomalies from the network statistical data \\ \hline
[33] & Method based on link quality and node reliability & Suitable for trust models that propagate trust values and do not consider the trustworthiness of the recommendation information; accuracy is low \\ \hline
[2] & Selective re-evaluation method & Risk assessment values are not involved in the calculation of trust values, which avoids bad-mouthing attacks, but re-evaluation increases latency and burden \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Classification and comparison of indirect trust evaluation methods. \[FT=\alpha\cdot DT+\beta\cdot IT \tag{12}\]  
where \(IT\) represents the indirect trust value of evaluated node \(j\), \(k\) represents the number of recommenders, \(DT(m)\) represents the direct trust of node \(j\) evaluated by recommender \(m\), and \(\alpha\) and \(\beta\) are the weights for direct trust and recommended trust, respectively, with \(\alpha+\beta=1\). The choice of weights depends on the specific application. _Weighted Average_  
Other models use a weighted average method to integrate the trust values provided by all recommenders.