## 5 Performance Evaluation
### Impact of Selfish Nodes on All Performance Metrics  
The observed results of the EMS technique were compared to techniques that did not undertake selfish node detection to determine the performance of the proposed EMS, as shown in Figure 1. The percentage of delivered packages is the first step in this investigation. As seen in Figure 5a, the package delivery probability decreases as the number of selfish nodes grow. The EMS, on the other hand, attempts to mitigate the negative effects of selfish nodes. This can be verified by comparing the package delivery probability of EMS with those methods that take no measures towards selfish nodes. When analyzing both techniques when 20% of nodes behave selfishly, as can be seen in Figure 1, the EMS enhances package delivery probability by about 28%, 23%, 25%, 27%, 28%, 26%, 34%, 32%, and 33% (for 20, 30, 40, 50, 60, 70, 80, 90, 100 mobile nodes, respectively). Moreover, in the worst-case sort of situation (80% selfish nodes), EMS helps to minimize the effect of selfish nodes, which will increase package delivery probability by 12%, 12%, 14%, 13%, 13%, 13%, 13%, 12%, and 12% (for 20, 30, 40, 50, 60, 70, 80, 90, 100 mobile nodes, respectively), particularly in comparison to a method that takes no action against selfish nodes. EMS is not just useful when there are selfish nodes in the network. When  
Figure 5: Impact of a Varying Number of Selfish Nodes. (**a**) Package Delivery Probability; (**b**) Package Delivery Delay; (**c**) Energy Consumption; (**d**) Number of Dropped Packages. compared and contrasted to a strategy where no reward is offered to cooperative nodes (i.e., nodes that do not deviate from the policy), the suggested cooperative scheme also helps to enhance the package delivery probability that can be seen in Figure 1a. The EMS then improves the package delivery probability by roughly 14%, 15%, 15%, 16%, 16%, 16%, 16%, 15%, 15%, and 16% (for a number of mobile nodes of 20, 30, 40, 50, 60, 70, 80, 90, 100, respectively). This is because, in the EMS scheme, there are \(N\) nodes to exchange their views on other nodes' routing behavior with one another, allowing them to identify a substantial percentage of selfish nodes. When compared to a technique for which no selfish node identification is conducted, which can be seen in Figure 1b, the EMS also obtains good results in terms of the package average delivery delay, as shown in Figure 5b. It indicates that EMS can send packages faster, which is much more obvious when the network has 60 or more mobile nodes. When 20% of selfish nodes are taken into account, the EMS delivers packages usually 22, 22, 22, 21, 21, and 21 min earlier (for 50, 60, 70, 80, 90, and 100 mobile nodes, respectively). Furthermore, in the worst-case situation (80% selfish nodes), the EMS handles packages that arrive 15, 15, 18, 18, 18, and 21 min earlier. It takes a very long time for a package to reach the destination node when the amount of selfish nodes grows in the network. When there is a selfish node in the network, packages will be discarded or significantly delayed, forcing the network to re-transmit the data packets. Re-sending data packets wastes network power, shortens the lifetime of the network and increases packet delivery delay. The package delivery delay is minimized in EMS because selfish nodes are detected quickly. The effectiveness of the EMS is examined in terms of energy consumption and package drop rate. For such a reason, the energy is taken into account first. The energy consumption does not vary considerably as a result of the EMS strategy, as shown in Figure 5c, despite the rise in the number of selfish nodes. When compared to the results obtained when no measures are taken over selfish nodes, as shown in Figure 1c, this is a substantial improvement. The EMS reduces the energy consumption by nearly 6, 7, 7, 8, 8, 8, 8, 9, and 9 packages (20, 30, 40, 50, 60, 70, 80, 90, and 100 mobile nodes, respectively) in the worst-case situation (80% of nodes behaving selfishly).