## 4 Challenges
### Weighting Trust Evidence Metrics  
Trust assessment involves allocating specific weights to the various evaluation metrics used. Most trust models rely on the WSN user to determine the correct weights [10; 18; 30; 35]. However, this approach is not practical as it requires an advanced understanding of the specific method used for trust evaluation. Thus, some authors use only the arithmetic mean method to integrate various trust metrics [2].  
To meet the challenge, [PERSON] et al. [14] proposed the following method for assigning weights: First, a trust threshold is predefined. If the trust values of the three trust metrics, i.e., the communication trust metric (CMT), energy trust metric (ET), and data trust metric (DAT), are either above or below this threshold, the weights are evenly distributed among the three metrics. If some of the trust metric values are above the threshold and some are below, the metrics below the threshold are evenly redistributed with weights, while the metrics above the threshold are ignored to prevent the masking of malicious activity. This approach prevents attacks manifested in one trust evidence aspect from being masked by good performance in other aspects. However, the proposed trust model includes only three trust evidence metrics and, therefore, addresses only some DoS attacks. Second, assigning equal weights to trust metrics is not very accurate as the functionalities and the performance expectations of WSNs may differ significantly (e.g., some networks require higher data accuracy, whereas others require high volumes of network traffic).  
[PERSON] et al. [33] used the softmax function based on ML principles to achieve dynamic weight allocation among various trust metrics. The weights of the trust metrics can be flexibly adjusted according to the differences between the trust metric values and the set thresholds, but the setting of thresholds and fine-tuning parameters still requires human intervention. Additionally, parameter selection is challenging.  
[PERSON] et al. [28] used a method combining fuzzy theory and the analytic hierarchy process to achieve fuzzy comprehensive dynamic weight evaluation. This calculation process includes four steps. First, the three trust metrics CMT, DAT, and ET are mapped to six fuzzy subsets \(U=\{u_{1},\ u_{2},\ u_{3},\ u_{4},\ u_{5},\ u_{6}\}\), representing six different trust levels from complete distrust to complete trust. Second, the trapezoidal membership function is used to calculate the membership degree of each trust factor in the six fuzzy subsets, forming the membership matrix \(E_{ij}=[e_{ij}]\). Third, a non-increasing function \(\lambda_{i}(T_{i})\) regarding trust values is introduced to achieve variable weights \(w_{i}=\frac{\lambda_{i}}{\sum_{j=1}^{N}\lambda_{j}}\), i.e., when \(T_{i}\) is smaller, the weight \(w_{i}\) increases, ensuring that the impact of low trust factors is more significant. Additionally, it is necessary to set the basic weights \(w_{mi}\) (referring to the initial weights of eachtrust factor under normal circumstances) and the upper weight limits \(w_{0i}\) (used to amplify the impact when a certain trust factor is too low). Finally, the comprehensive trust value is calculated based on the weight vector \(W=\{w_{1},w_{2},w_{3}\}\) and the membership matrix \(E_{ij}\) as \(V=W*E_{ij}\); the comprehensive trust value is de-fuzzified using the centroid method. This method involves analytic hierarchy fuzzy logic operations and matrix calculations. Its computational complexity is higher compared to traditional weighted average methods. Parameter tuning is also a challenge. We conducted a comparative analysis of the three methods for weight allocation to trust metrics and summarized their pros and cons (Table 7). As shown, none of the methods offers a lightweight reliable approach for dynamic weight allocation that does not require human intervention. This is still a significant impediment to the development of trust models for the protection of WSNs.