## 2 Background and Related Works
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Refs** & **Technique** & **Key Idea** & **Strengths** & **Weaknesses** \\ \hline
[19] & Clustering method & \begin{tabular}{c} Uses two heads in order to \\ balance energy between \\ nodes \\ \end{tabular} & \(\bullet\) & \
\begin{tabular}{c} Reducing the amount of \\ intra-cluster \\ communication \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Increase complexity \\ of routing filtering \\ \end{tabular} \\ \hline
[20] & Clustering method & \
\begin{tabular}{c} CH is selected based on \\ various factors \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Extend the network \\ lifetime & \(\bullet\) & High complexity \\ \hline
[33] & Clustering method & \begin{tabular}{c} CH is selected based on the \\ highest residual energy \\ \end{tabular} & \
\begin{tabular}{c} Reduce nodes power \\ consumption \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} confidentiality and \\ integrity are not \\ supported \\ \end{tabular} \\ \hline
[26] & Automated encryption & \begin{tabular}{c} Balance nodes security \\ and power \\ \end{tabular} & \(\bullet\) & \
\begin{tabular}{c} Reduce power \\ consumption \\ Support different types of \\ internal and \\ external attacks \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Priority data is not \\ considered \\ \end{tabular} \\ \hline
[29] & lightweight trust algorithm & \
\begin{tabular}{c} Monitoring WSN node’s \\ parameters \\ \end{tabular} &
\begin{tabular}{c} Support different types of \\ internal attacks \\ \end{tabular} & \(\bullet\) & Slow response time \\ \hline
[34] & Adaptive trust-based & \begin{tabular}{c} Adjust the security level \\ based on the trust level of \\ nearby vehicles \\ \end{tabular} & \(\bullet\) & \
\begin{tabular}{c} Improve the QoS for \\ networks \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Not suitable for \\ ad-hoc networks \\ \end{tabular} \\ \hline
[35] & Centric trust framework & \
\begin{tabular}{c} Using decision tree \\ classification and artificial \\ neural networks \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Improve the accuracy of \\ the trust model \\ \end{tabular} & \(\bullet\) & Costly \\ \hline
[30] & Trust metrics framework & \
\begin{tabular}{c} k-meant clusters to \\ categorize interactions as \\ reliable and untrustworthy \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Improve the accuracy of \\ the trust model \\ \end{tabular} & \(\bullet\) & Costly \\ \hline
[36] & Trusted and Secure & \begin{tabular}{c} Cuckoo search algorithm \\ Routing \\ \end{tabular} & \begin{tabular}{c} Provides the secure \\ routing path \\ \end{tabular} & \(\bullet\) & Packet delivery ratio \\ \end{tabular} & \(\bullet\) & \
\begin{tabular}{c} Minimizes the probability \\ of malicious nodes \\ \end{tabular} & \(\bullet\) &
\begin{tabular}{c} Packet delivery ratio \\ is lower \\ \end{tabular} \\ \hline
[37] & Energy Trust Model & \begin{tabular}{c} establishing a trustworthy \\ communication pathway \\ \end{tabular} & \
\begin{tabular}{c} Balance power \\ consumption between \\ privacy and energy \\ Support end-to-end{tabular} & \(\bullet\) &
\begin{tabular}{c} Higher computational \\ complexity. \\ Higher energy \\ overhead \\ \end{tabular} \\ \hline
[38] & Hierarchical trust &
\begin{tabular}{c} Balanced weight subcluster \\ formation uses Fuzzy \\ S-means to the \\ CH-identification scheme \\ \end{tabular} & \(\bullet\) & Cover a high variety of \\ malicious attacks. & \(\bullet\) & Complexity is high \\ \hline
[39] & Trust clustering model &
\begin{tabular}{c} Using GA to choose the \\ appropriate CH \\ \end{tabular} & \(\bullet\) & Improve the power \\ consumption & \(\bullet\) & Costly \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Review of the trusted-based methods.