# Utilising Acknowledge for the Trust in Wireless Sensor Networks
## 5 Results and Analysis  
To assess the effectiveness of the suggested methods, we put our new model to the test against a number of random-topology WSNs using the TRMSIM-WSN simulator [37]. Some modifications have been made to satisfy the proposed approach requirements. Sensors are distributed in a (100 \(\times\) 100) \(m^{2}\) area, and each of them has a sensing domain of 10 m. We run the simulator under the same Configuration Parameters for all cases, networks' number = 50, the executions' number over each network = 100, i.e., every client requests service 100 times. Additionally, the proportion of the client is equal to 15 percent, the relay is equal to 10 percent, and malicious sensors range from 5 percent to 35 percent; the rest are benevolent sensors and the nodes' number (equal to 100 sensors) in each network. We will run the simulator for 300 s. For the purpose of evaluating the efficacy of the proposed model, we compute the following performance metrics:  
Figure 2: Flowchart of TAT Routing Protocol. 1. Malicious Detection Ratio (MDR): The percentage of sensors in the WSN has been identified as misbehaving. 2. Accuracy Standard deviation (SD). 3. Average Path Length (APL) or the number of hops to trusted destinations. 4. Average Energy Consumption (AEC) (\(\mu\)J): It measures the needed AEC of our proposed approach. To evaluate the performance of the proposed model and verify its effectiveness, two simulations were conducted in two different scenarios:  
1. In the first scenario, we studied the impact of the network density on the performance of the proposed protocol. For that, the network size differs in Figures 3-5. 2. In the second scenario, we compared the proposed protocol (TAT) with other protocols (BTRM and PeerTrust); thus, the network size is the same in Figures 6 and 7. First, we study the impact of the misbehaving sensors on the suggested technique, i.e., we evaluate the suggested technique when the percent of the misbehaving sensors increased in the static scenario. Then, we will compare the suggested model results with other existing models. Figure 3 shows the variation in MDR vs. the percent of misbehaving nodes in the WSN for different density values of the WSN. As shown in Figure 3, it is demonstrated that when the MDR decreases, the number of misbehaving sensors increases. Moreover, the proposed model achieved MDR above  
Figure 5: APL vs. % malicious sensors in WSNs. Figure 6: MDR for TAT, PeerTrust, and BTRM Models. Figure 7: APL for TAT, PeerTrust, and BTRM Models. 60% when the number of misbehaving nodes was not greater than 25%. Therefore, from Figure 3, we can deduce that the suggested method is scalable. The variation in SD of MDR vs. the percent of misbehaving nodes in WSN is presented in Figure 4. In addition, as given in the figure, although the WSN size is 300 sensors, which is a large number of sensors, the standard deviation remains low (less than 3.5%). A shorter route towards trustworthy destinations entails less involved sensors; as a result, there is less global use of resources such as energy. The variation in the APL vs. the percent of misbehaving nodes in WSN is presented in Figure 5.  
For a given WSN, the APL increases with the percentage of malicious nodes increases. So the route to the server that has the service or destination will be longer, and APL will increase. As given, while the number of misbehaving nodes is not more than 25%, any trustworthy destination is never reached at more than four hops, on average. Table 2 shows the AEC in the networks vs. the percentage of the misbehaving nodes in the WSN. Two direct conclusions can be deduced from Table 2. Firstly, the higher the density of WSNs is, the higher the energy needs. However, as the number of misbehaving nodes increases, so does the power consumption. Due to the misbehaving nodes in a WSN that are a majority, it is harder to identify a trusted node, and as a result, more messages are transmitted. Therefore, to send out more messages, more energy is consumed.