## 4 Challenges
### Loss of Trust Information  
Managing information loss refers to the problem of storing and distributing trust information effectively and efficiently. Trust models in WSNs can be distributed [5] or centralized [7]. In a distributed trust model, each node computes and stores its own trust value. If a node fails, the impact of the subsequent loss of trust information will be limited to the neighbor not being able to evaluate the failed node. A typical method for managing trust information in a distributed trust model is storing a local copy of the network at each node along with a node backlist [24]. When a node broadcasts a DIO message to its neighbors, the node appends to it its own trust value and the blacklist. A somewhat similar approach was proposed by [PERSON] et al. [35], which is also suitable for a distributed \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author, Year** & \multicolumn{2}{c}{**Weight Assignment Methods**} & \multicolumn{2}{c}{**Pros and Cons**} \\ \hline  & Cases & Trust Values & Weights & Pros: prevents attacks \\  & & \(DAT\geq 0.5\) & \(w_{dat}=1/3\) & manifested in one trust \\ [PERSON] et al., & \multirow{2}{*}{1} & \multirow{2}{*}{2} & \(\mathit{CMT}\geq 0.5\) & \(w_{cnt}=1/3\) & evidence aspect from being \\
2019 [14]; [PERSON] et al., & & \(\mathit{ET}\geq 0.5\) & \(w_{et}=1/3\) & masked by good performance \\  & & \(\mathit{DAT}<0.5\) & \(w_{dat}=1/3\) & in other aspects \\  & & \(\mathit{CMT}<0.5\) & \(w_{cnt}=1/3\) & Cons: only supports three trust \\  & & \(\mathit{ET}<0.5\) & \(w_{et}=1/3\) & evidence metrics; not very \\  & & & The weight of the trust & accurate \\  & & \(\mathit{DAT,\ CMT,\ or ET}\) & factor that is greater & \\  & & have mixed values & than 0.5 will be assigned & \\  & & greater and lower & 0 and the remaining & \\  & & than 0.5 & weights will be assigned & \\  & & & equally & \\ \hline [PERSON] et al. [33] & & & Pros: dynamic weight allocation & \\  & & & Cons: requires human intervention; parameter selection is challenging \\ \hline  & Fuzzy subsets \(U=\{u_{1},\ u_{2},\ u_{3},\ u_{4},\ u_{5},\ u_{6}\}\); Membership matrix \(E_{ij}\); Variable weights \(w_{l}=\) & Pros: dynamic weight allocation; prevents attacks & \\  & & & manifested in one trust & \\ [PERSON] et al. [28] & \(\frac{\lambda_{i}}{\sum_{j=1}^{M}\lambda_{j}}\), the basic weights \(w_{mi}\), and the upper weight limits \(w_{0i}\); Comprehensive trust \(V=W*E_{ij}\); De-fuzzify the comprehensive trust value using the centroid method. & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification and comparison of three methods for weight allocation to trust metrics. trust model: WSN nodes record trust evidence in their traffic profiles and share them with each other. In a centralized trust model, trust value computation occurs at the nodes acting as heads of node clusters or at the base station. Trust information is stored both at the cluster head (base station) and at the nodes. The trust evidence collected by nodes and/or preliminary trust evaluation results are sent to the respective cluster heads (base station). The cluster head (base station) stores these records, uses them to calculate, aggregate, and maintain the trust values of each member of the cluster, detects malicious nodes based on trust values, and takes countermeasures. Several methods for recording and propagating trust information in centralized trust systems were proposed in the reviewed literature. In [16], the cluster head is responsible for gathering trust evidence from cluster members and forwarding it to the base station, which maintains a comprehensive trust table. A similar table is proposed in [13], which is created and updated at the cluster head. For SDWSNs, [PERSON] et al. [4] proposed using the number of successfully forwarded/failed to forward data and control packets and the number of new flow packets as metrics to calculate the node's trust value locally and to forward it to an aggregator node.