## 6 Performance Evaluations
### Simulation Results
#### Deadline miss ratio evaluation under the second scenario  
For the second scenario, the effectiveness of the suggested technique is evaluated in this set of experiments and contrasted to earlier research SMRP [15] and EERP [23]. The experiment involves varying the average traffic rate and deploying different number of sink nodes in the network. Figure 7: Influence of increasing number of sink nodes on deadline miss ratio under the first scenario. The deadline miss ratio in the first simulation experiment is compared to the second scenario's average traffic rate. This experiment adjusts the average traffic rate from three to 11 packets per second, using three sink nodes and a 700 ms deadline. Figure 9 shows how delays change with traffic volume. This figure demonstrates that the suggested solution outperformed others despite increasing network traffic. The proposed algorithm outperforms SMRP and EERP in the first scenario for the following reasons. As with SMRP, the proposed approach uses a modified environmental impact metric to avoid risk zones and find safer data transmission routes. The proposed approach seeks to create data routes less likely to be cut off due to environmental factors, resulting in more reliable links and reduced delivery delay. The obtained results further validate that the proposed environmental impact metric leads to safer routing paths than those computed by SMRP. This explains why the proposed approach superior to the others. Regarding the EERP algorithm, it does not consider the environmental impact on the routing performance, hence a poor performance in terms of packet delivery ratio, which in turn increases the delivery delay and, thus, the deadline miss ratio. Therefore, it has the worst performance in terms of deadline miss ratio. The second experiment examines the deadline miss ratio with varied numbers of sink nodes. This experiment varies the number of sink nodes from one to three while keeping the average traffic rate and packet deadline at three packets per second and 700 ms, respectively. Figures 9 and 10 demonstrate how missed deadlines vary with sink nodes. The deadline miss ratio decreases with more sink nodes, as shown in the figure. As the number of sink nodes increases, data packets travel shorter distances, reducing delivery delays. As a consequence, the deadline miss ratio reduces. The proposed algorithm has the lowest deadline-miss ratio. In addition to the reasons mentioned above, the proposed algorithm is environment-aware, which helps avoid danger zones. Thus, the environmental effect is less likely to cut off routing paths, minimising packet loss and delivery delay. Figure 8: Influence of increasing deadline on deadline miss ratio under the first scenario. Although the SMRP is also an environment-aware routing algorithm, the results are evidence that the proposed environmental impact metric helps to discover safer routing  
Figure 10: Influence of increasing number of sink nodes on deadline miss ratio under the second scenario. Figure 9: Influence of increasing average traffic rate on deadline miss ratio under the second scenario. paths than those computed by SMRP. In the case of EERP, the impact of the external environment on the routing performance is not considered, which implies that the packet loss ratio and the delivery delay will be increased. Hence, its deadline miss ratio becomes worse than the proposed SMRP algorithms. The third simulation experiment analyses deadline miss rates with varied deadline values in scenario 2. This experiment varied the deadline from 700 to 1000 ms while keeping sink nodes and traffic rate at three nodes and three packets per second. Figure 11 shows how the deadline miss ratio increases for the second scenario. The deadline miss ratio decreases as the deadline value increases, as more packets reach sink nodes before their deadlines. The suggested algorithm has the lowest deadline miss rate.