# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 5 Results and Analysis
The proposed TAT technique is compared with two existing models, PeerTrust [26] and BTRM [27]. Figure 6 shows the comparison between TAT, PeerTrust, and BTRM models regarding the Malicious Detection Ratio (MDR%). From Figure 6, MDR's precision for all methods decreases as the percent of misbehaving sensors in the WSN increases because the percent of trusted sensors in the neighborhood for each node will decrease. Consequently, the precision of selection trustworthy next hop will decrease. Moreover, we can see that the performance of the proposed approach overcomes other models since we use monitoring tools at the link level (ACK\({}_{\text{DLL}}\)) and for the entire path (ACK\({}_{\text{TCP}}\)). The result of a comparison between the proposed approach and other approaches according to the APL and the percent of malicious sensors in the network are shown in Figure 7.  
The value of APL rises as the number of malicious sensors in the network grows, as shown in Figure 7. Consequently, the number of malicious sensors in the network is increasing, which means there will be fewer number of good nodes surrounding each sensor, reducing the chance of discovering to send to in the next hop. Moreover, it is clear that the value of the average path length for the proposed approach is better than other approaches, because the MDR of the proposed model is better than other models. Therefore, the number of trust neighbors will be higher; as a result, the probability of finding trust next hop is increased. Table 3 shows the average values of total energy consumption for all approaches. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Malicious\%** & **Network Size = 50** & **Network Size = 100** & **Network Size = 300** \\ \hline
5 & \(1.8\times 10^{3}\) & \(1.4\times 10^{6}\) & \(1.4\times 10^{8}\) \\
10 & \(1.9\times 10^{3}\) & \(1.7\times 10^{6}\) & \(1.6\times 10^{8}\) \\
15 & \(2.1\times 10^{3}\) & \(1.9\times 10^{6}\) & \(1.7\times 10^{8}\) \\
20 & \(3.1\times 10^{3}\) & \(3.3\times 10^{6}\) & \(1.9\times 10^{8}\) \\
25 & \(3.4\times 10^{3}\) & \(7.3\times 10^{6}\) & \(2.2\times 10^{8}\) \\
30 & \(6.4\times 10^{3}\) & \(12.3\times 10^{6}\) & \(8.3\times 10^{8}\) \\
35 & \(9.9\times 10^{3}\) & \(30\times 10^{6}\) & \(21\times 10^{8}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Total AEC (μ) in the network. From Table 3, when the percentage of misbehaving nodes in a WSN increases, the energy consumption will increase. In other words, the broken links will increase. Additionally, more control packets must be generated, increasing energy consumption. Moreover, we can see that the suggested approach is more efficient in saving energy than other approaches since the MDR of the proposed approach is better than other approaches. In addition, the length of paths in the presented approach is shorter than other approaches; consequently, the consumption of energy will be decreased. As a summary of the results, the proposed technique is able to detect and isolate the malicious sensors along the route from the source to the destination--therefore, creating a free-malicious- end-to-end route. Moreover, as can be seen, from the results, using the proposed technique, the malicious detection ratio (MDR) increases, and the Average Energy Consumption (AEC) decreases, expanding the network's throughput. In addition, A shorter route towards trustworthy destinations entails less involved sensors; as a result, there is less use of resources such as energy.