## 5 Results  
Due to features such as changeable topology and openness, MANET is vulnerable to many attacks. This leads to the exploitation of MANET in the presence of malevolent, or selfish nodes through many forms of assaults. Such nodes affect MANET routing performance, such as the delivery ratio of packets. The need for a factor of trust between nodes of communication is therefore justified in this proposed model. The proposed model is implemented in the NS2 simulator that establishes a MANET, and routing is performed on trusted nodes by considering the neighbour feedback. Every node undergoes multi authentication to strictly avoid malicious nodes so that the network's performance will be increased. The proposed Neighbour Feedback-based Trusted Multi Authenticated Node (NFbTMAN) Routing Model is compared with the Novel Energy Efficient Trust AwareRouting (NETAR) model. The proposed model considers parameters such as Trust Factor Generation Time Levels, Route Identification Time Levels, Neighbour Feedback Assessment Level, Multi-Authentication Timed Levels, Packet Delivery Rate. The parameters considered for network formation are depicted in Table 1.  
Network trust evaluation is utilised to promote secure and trustworthy networking by helping nodes collaborate in a trustworthy manner. However, there are still many difficulties and flaws with many trust management models proposed for the MANET. The proposed model trust factor generation time levels are compared with the traditional model, and the results are depicted in the Figure 4. Table 2 represents trust factor generation time levels. Changes in route topology occur because of node mobility and, because of this, the system's architecture remains unforeseen for some time. Due to the decentralised nature of such networks, network communications must identify routes with high security. In the route discovery phase, the trust calculation is performed to include trusted nodes. The proposed model route identification time levels are compared with the traditional model, and the results are indicated in Figure 5. Route identification time levels are shown in Table 3. \begin{table}
\begin{tabular}{c c} \hline
**Parameter** & **Value** \\ \hline Number of simulated Nodes & 20-40-60-80-100 nodes \\ \hline Area size of topography (m) & 500 m \(\times\) 500 m \\ \hline Radio range & 150 m \\ \hline Packet size & 1000 byte \\ \hline Send rate of traffic & 4 packets/s \\ \hline Traffic type & cbr \\ \hline Number of traffic sources & 4 \\ \hline Pause Time (s) at simulation & lOs \\ \hline Simulation Time & 80 s \\ \hline Simulated Routing Protocols & AODV \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation Parameters. \begin{table}
\begin{tabular}{c c c} \hline
**Network Nodes** & \multicolumn{3}{c}{**Trust Factor Generation Time Levels**} \\ \cline{2-3}  & **NFbTMAN Model** & **NETAR Model** \\ \hline
10 & 20 & 20 \\ \hline
20 & 28 & 30 \\ \hline
30 & 30 & 38 \\ \hline
40 & 34 & 42 \\ \hline
50 & 40 & 50 \\ \hline
60 & 40 & 58 \\ \hline
70 & 45 & 61 \\ \hline
80 & 50 & 75 \\ \hline
90 & 52 & 88 \\ \hline \end{tabular}
\end{table} 
Table 2: Trust Factor Generation Time Levels. The proposed model considers neighbour feedback to establish a route, and the feedback assessment levels are represented in Figure 6. The feedback will help analyse the  
Figure 4: Trust Factor Generation Time Levels. Figure 5: Route Identification Time Levels. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Network Nodes**} & \multicolumn{2}{c}{**Route Identification Time Levels**} \\ \cline{2-3}  & **NFbTMAN Model** & **NETAR Model** \\ \hline
10 & 8 & 32 \\ \hline
20 & 14 & 38 \\ \hline
30 & 18 & 42 \\ \hline
40 & 22 & 48 \\ \hline
50 & 28 & 53 \\ \hline
60 & 32 & 57 \\ \hline
70 & 40 & 60 \\ \hline
80 & 43 & 65 \\ \hline
90 & 50 & 75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Route Identification Time Levels. node behaviour for considering only trusted nodes in finalising the route for secure data transmission.