# Towards Strengthening the Resilience of IoV Networks--A Trust Management Perspective
## 3 Trust Management in the Internet of Vehicles
### Trust Models
#### 3.2.4 Trust Model Simulation Method  
The evaluation of the trust model in VANET mostly adopts simulation methods. Specifically, most of these suggestions use the NS-2 simulator [42]. Additionally, some proposals adopt other existing simulation tools, such as GloMoSim 2.03 [11], Matlab [12; 27; 38; 57; 58],  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**No.** & **Evaluation Parameters** & **Simulation Tools** \\ \hline
[11] & Precision(P), recall (R), communication overhead & GloMoSim 2.03. \\ \hline
[27] & FMR (familiarity), PDR & CRAWDAD dataset. MATLAB. \\ \hline
[28] & Recommendation usage rate, FNR, prediction rate & Undefined. \\ \hline
[29] & Influence of node behavior on direct trust value, influence of the integration of direct trust and recommended trust on PDR, influence of the time sliding window and time decay, function on the direct trust value & Mobile model. \\ \hline
[30] & Precision and recall & NS2. \\ \hline
[31] & Performance quality level (PQL), feedback accuracy level (FAL) & MATLAB. \\ \hline
[35] & End-to-end delay (E2 ED), event detection probability (EDP), anomaly ratio (AR), FFR, trusted and untrusted packets & Veins. \\ \hline
[36] & FPR, TPR, precision, recall & Network Simulator 2.35 (NS 2.35), the Open Street Map (OSM) database. \\ \hline
[37] & FPR, TNR & SIGCOMM-2009 conference, which is available in CRAWDAD. \\ \hline
[38] & Undefined cases (UND), true positive rate (TPR) & MATLAB. \\ \hline
[53] & TPR, FPR, trust computation error & NS 2.35. \\ \hline
[54] & Accuracy, recall, precision, F1-measure & CICIDS2017 dataset MATLAB R2019a on Windows 10 (random forest and coresets models). \\ \hline
[45] & The rate of untrust packets, the rate of trust packets, packet delivery ratio & Network Simulator Omnet ++ and Veins (Vehicle in Network). \\ \hline
[46] & Travel time, accuracy, CO\({}_{2}\) emissions, communication overhead & Veins. \\ \hline
[55] & Pairwise orderedness, threshold of trust value, TPR, TNR & Veins. \\ \hline
[56] & Precision, recall, F1-score & CRAWDAD dataset. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Existing evaluation parameters and simulation methods.  
and Veins [36; 46; 54; 55; 59; 60]. In addition, some papers have used C++ or Java programming languages for simulation. Table 2 summarizes the simulation tools in detail. Of course, in some papers, the trust model is not evaluated by simulation but only by theoretical analysis and discussion. For example, in [41], the author proposed a global trust-building scheme based on reputation (RGTEs). This scheme introduces a solution to realize the secure sharing of trust information in VANET by using statistical rules, which makes it more efficient and accurate for VANET to build trust in a rapidly changing environment. In addition, according to the real-time reputation of the network, the author used a dynamic threshold to detect malicious nodes. Many previous studies only used hypothetical methods in the simulation stage, and the trust model was not evaluated in the real car networking environment, which are limitations that we should improve upon in the future.