# Recommendation-Based Trust Evaluation Model for the Internet of Underwater Things
## 7 Experiments and Performance Evaluation  
In this section, we outline the methodology and experimental setup employed to rigorously evaluate the performance of the proposed recommendation evaluation process within an underwater network environment. To conduct a realistic simulation environment, we developed a trust-based model integrated with the AquaSim-NG network simulator [41], a discrete-event simulation tool built on NS-3 [42]. This approach was selected for its ability to accurately simulate the distinctive characteristics of underwater communication networks, utilizing the sound speed profile outlined in [43] and incorporating environmental variations based on data in [44]. Additionally, we employed the Ekman mobility model to replicate the dynamic behaviour of water currents. The network was configured with multiple randomly placed underwater nodes, adhering to the specifications detailed in [33]. Within this network, random nodes initiated data packet generation following an on/off method, with data exchanged between nodes and collected by a sink node positioned at the sea surface. Sending separate packets for recommendations introduces communication overhead, which can affect overall network performance. This impact depends on several factors, including the frequency of trust updates, the application's latency requirements, and the underlying routing protocol. In these experiments, we embedded the recommendation information within the packet headers.  
Within this trust-based model, we employed the MATMU model to compute the direct trust of each node [33]. The proposed recommendation evaluation techniques were applied to assess indirect trust based on 1-hop recommendations. We introduced varying proportions of malicious and dishonest recommendations in each simulation test. Based on our experiments, the thresholds for deviation and violation were set at 0.1 and 2, respectively, as these values were reasonable and enhanced the model's accuracy.  
The methodology is designed to comprehensively assess the effectiveness, attack resistance, and comparative performance of the proposed approach. Initially, we focused on evaluating the feasibility of the recommendation evaluation process in detecting misbehaving nodes. This step was essential to establish the baseline performance of the model. Subsequently, we examined the attack resistance of the network by simulating scenarios both with and without the proposed recommendation filtering method. This analysis was crucial to demonstrate the robustness in mitigating malicious behaviour. Finally, we conducted a comparative analysis against existing methods to evaluate the relative performance of the proposed approach. This comparison was justified by the need to benchmark our method against current models, thereby providing a clear understanding of its advantages and limitations.  
Details of the simulation parameters and specific configurations are presented in Table 1. The following subsections proceed to provide a thorough and justified evaluation of the proposed recommendation process, highlighting its strengths and areas for potential improvement.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline \multicolumn{2}{c}{**Simulation Variables**} & \multicolumn{3}{c}{**Node Variables**} \\ \hline Simulation Time & 1800 s & Number of Nodes & 20 \\ \hline Surface Wind & 8.5–27.2 knots & Initial Energy & (10,000–70,000) watt \\ \hline Propagation model & Range-based & Transmission Range & 100, 120 m \\ \hline \multicolumn{2}{c}{**Network Variables**} & \multicolumn{3}{c}{**Recommendation Variables**} \\ \hline Routing Protocol & Vector Forwarding & \(d_{h}\) & 0.1 \\ \hline Data Rate & 1000 bps & \(v_{h}\) & 2 \\ \hline Packet Size & 40 bytes & Attack \% & 10–50\% \\ \hline Carrier Frequency & 25 kHz & Recommendation request & each 60 s \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters.