## 5 Simulation Results
### Performance Evaluation  
To evaluate our study versus existing algorithms, the simulations were carried out in terms of performance metrics with their results being presented below. 1. Number of alive nodes in the network: The alive nodes metric is taking into consideration the first node's starting to die and all the nodes's dying. We have tested our proposed algorithm over the other protocols varying two scenarios, including scenario 1 and scenario 2 in terms of the number of alive nodes in the network. We have used 200 nodes that are distributed in the network. As seen in Figure 10(a), the nodes starts to die after the 1224 th round, while using our algorithm in the scenario 1. This result validates that the proposed method outperforms the existing algorithms for longer rounds. This is because the proposed algorithm is able to utilize the network energy in a balanced manner. Additionally, as shown in Figure 10(b), all of the algorithms provide less performance than before when we simulate them in scenario 2. However, our algorithm is still the best performance in comparing the other algorithms. We notice that it requires more sensor nodes deployed in the sensing area to continue for longer rounds in large-scale wireless sensor networks. 2. Total remaining energy of the network: By this performance metric, the total residual energy over the lifetime of the network is calculated. We have comprehensively simulated the performance of the proposed algorithm over the other algorithms in both scenarios in terms of the remaining energy of the network versus the number of rounds. Figure 10: Determination of the \(\infty\), \(\beta\), and \(\delta\) values according to normal distribution function. structure, and can be delivered the packets to the destination more accurately when compared to others. Trajectory length travelled by MS: This is total routing path length visited by MS. We have experimented all of the algorithms according to trajectory length performance parameter varying from 200 to 1000 sensor nodes that were deployed randomly in the network. Our proposal has generated the minimum travelling path among the existing algorithms due to the fact that the proposed energy-delay and the transmission aware routing protocol is able to determine the best routes for each period. Thanks to the single-hop communication of MS with CH, and visiting the CHs in their transmission range, the trajectory length is shortened. For instance, as illustrated in Figure 13(a),b, the proposed algorithm achives the best results almost \(0.6\times 10^{4}\) and \(1.15\times 10^{4}\) path lengths for 200 nodes in scenario 1 and scenario 2, respectively. However, as the number of nodes increases, the travelling length extends in both scenarios. Additionally, Figure 13(b) depicts that the trajectory lengths for all of the algorithms are extremely higher values than that of presented in Figure 13(a), because of the number of travelling and delivering packet hops in the large- size networks. We ran the proposed algorithm for 200 nodes in scenario 1 and obtained the best trajectory that was travelled by MS, as seen in Figure 15. We do not present the simulation graph of the mobile sink trajectory for scenario 2 to the readers, because the network graph is found as very complex visulation. Actually, we consider that presenting Figure 15 is sufficient in understanding the network model and the proposed methodology of our proposal combined scheme. Figure 13: Comparison of the proposed scheme with existing algorithms versus number of packets received by BS: (**a**) Simulation graph for scenario 1; and, (**b**) Simulation graph for scenario 2. * Packet delivery delay: This is the latency time to deliver data from the source node to the destination node (MS). The lower value means it gives better performance. As shown in Figure 16a, Packet delivery delay decreases in all of the algorithms as the mobile sink speed is about 5 m/s.