# Towards Tamper-Proof Trust Evaluation of Internet of Things Nodes Leveraging IOTA Ledger
## 2 Literature Review
### Direct and Indirect Metrics for Trust Assessment
\begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Trust Model** & **Storage** & **Ledger/Platform** & **Metrics Used** &
\begin{tabular}{c} **Consensus/Update** \\ **Mechanism** \\ \end{tabular} & **Attack Resilience** \\ \hline
[21] & Local & None & \begin{tabular}{c} User satisfaction, node \\ recommendations \\ \end{tabular} & \
\begin{tabular}{c} Weighted sum with \\ context adjustment \\ \end{tabular} &
\begin{tabular}{c} Low (context \\ spoofing possible) \\ \end{tabular} \\ \hline
[22] & Local & None & \begin{tabular}{c} Cross-validation \\ between UAV and \\ vehicle data \\ \end{tabular} & \
\begin{tabular}{c} Distributed \\ reputation propagation \\ \end{tabular} &
\begin{tabular}{c} Medium (dependent on \\ honest majority) \\ \end{tabular} \\ \hline
[23] & Local & None & \begin{tabular}{c} Honesty, energy level, \\ recommendations \\ \end{tabular} & \
\begin{tabular}{c} Continuous updates \\ with neighbor reports \\ \end{tabular} &
\begin{tabular}{c} Low (lacks attack \\ modeling) \\ \end{tabular} \\ \hline
[24] & Local & None & \begin{tabular}{c} Data quality, security, \\ referral trust \\ \end{tabular} & \
\begin{tabular}{c} Sliding time \\ window-based updates \\ \end{tabular} &
\begin{tabular}{c} Medium (filtering \\ included) \\ \end{tabular} \\ \hline
[25] & Blockchain-based & Ethereum & \begin{tabular}{c} Service quality, history \\ of data interactions \\ \end{tabular} & \
\begin{tabular}{c} PoW-based consensus \\ (Ethereum) \\ \end{tabular} &
\begin{tabular}{c} Medium (secure but \\ resource-intensive) \\ \end{tabular} \\ \hline
[26] & Local & None & \begin{tabular}{c} Community leader \\ ratings, node reputation \\ \end{tabular} & \
\begin{tabular}{c} Centralized cluster \\ aggregation \\ \end{tabular} &
\begin{tabular}{c} Medium (leader \\ compromise risk) \\ \end{tabular} \\ \hline
[27] & Blockchain & Hyperledger & \
\begin{tabular}{c} Behavioral history, \\ trust contracts \\ \end{tabular} & Smart contracts (PoA) &
\begin{tabular}{c} High \\ (ledger-backed trust) \\ \end{tabular} \\ \hline
[28] & Blockchain & Custom DLT & \begin{tabular}{c} Routing behavior, \\ transport delay, \\ consistency \\ \end{tabular} & \
\begin{tabular}{c} Decentralized \\ updates + DLT logs \\ \end{tabular} &
\begin{tabular}{c} Medium–high \\ (immutable audit trail) \\ \end{tabular} \\ \hline
[29] & Verifiable logs & Blockchain & \begin{tabular}{c} Historical baseline \\ deviation \\ \end{tabular} & \begin{tabular}{c} Cryptographic + \\ statistical verification \\ \end{tabular} & \begin{tabular}{c} High \\ (tamper-evident logs) \\ \end{tabular} \\ \hline Proposed scheme & Fully decentralized & IOTA Tangle & \begin{tabular}{c} Reliability, \\ QoS, centrality, \\ Tangle-based trust \\ \end{tabular} & \
\begin{tabular}{c} Local mean–median \\ consensus \\ \end{tabular} &
\begin{tabular}{c} High (Sybil, collusion, \\ eclipse resilience) \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of the recent literature. extensive contextual data, potential complexity in deployment, and incomplete evaluation against adversarial attacks in highly adversarial or data-scarce settings. While these approaches demonstrate the effectiveness of integrating direct and indirect trust metrics, it is crucial to know that expanding the range of metrics can impact system complexity and computational resources, particularly in dynamic and large-scale IoT environments. Additionally, the need for synchronization and continuous updates affect real-time performance and scalability.