# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Results
#### 6.3.3 Deadline Miss Ratio Evaluation  
In this series of experiments, the performance of the proposed method is assessed in terms of the deadline miss ratio in comparison to the SEA [18], EGTDA [19], and ACO-based techniques in [22], and EARP [24] for the situations indicated above under varying traffic rates. For the sake of evaluating such variations, it is assumed that the urgent data messages have varying deadlines which are determined by the priority of their respective zones. That is to say, the time limit for the transmission of urgent data messages originating from the region with the greatest priority is the shortest, and vice versa. The deadlines range from 100 milliseconds to 700 milliseconds. 1. Deadline miss ratio evaluation under the first scenario  
This experiment compares deadline miss ratio with average traffic rate. The simulation experiment is launched to examine this variance by raising the average traffic rate \(\lambda\) from three to nine packets per second. Figure 10 compares deadline miss ratios with various traffic rates. As shown in the figure, the proposed routing method in [18; 19; 22; 24] for urgent data gathering produced a lower deadline miss percentage than the PSO-based routing technique in [28]. The proposed routing method updates the desired delay of packets in each hop and picks cluster heads that can deliver the urgent data packet within its deadline. The relaying delay is then considered while picking the next forwarder from these cluster heads. It evaluates link quality to reduce retransmission latency. Such parameters reduce packet miss ratio. Figure 9: Influence of increasing average traffic rate on packets delivery ratio under the second scenario. Real-time data transmission is not included in [28]'s PSO-based routing algorithm, hence constrained latency requirements are not met. Moreover, packets in such an algorithm cannot avoid unreliable paths, causing many lost packets and increasing retransmission latency. So, deadline misses rise. However, according to the results in Figure 10, it is obvious that the proposed approach achieves the minimum deadline miss ratio compared to other works. The reasons behind such results are justified as follows:  
The proposed route planning considers the average distance between the mobile sink and urgent data cluster heads. This reduces the desired delay, which means that the urgent messages would reach the sink node within their deadlines, which in turn reduces the percentage of times that a deadline was missed. According to the information presented earlier, the priority of the zone is considered when determining the path that will be taken. The mobile sink as a result gathers the urgent data from the high priority zones first, resulting in an excessive avoidance of the routing of urgent data messages. It reduces delivery delays as a consequence, which in turn reduces the proportion of deadlines missed. Additionally, the movement delay is considered since the mobile sink's travel time may be prolonged, especially in large sensor fields, and some packets may be lost owing to the sensor nodes' limited buffer capacity. As a result, including such a setting results in a decrease in the delay brought on by the retransmission of lost packets. The proposed technique also takes into consideration the average distance between nodes inside the cluster when choosing cluster heads. By reducing end-to-end latency, this also lowers the proportion of deadlines that are missed. Additionally, the cluster heads take connection quality into account while making decisions, which reduces the number of packets lost and the possibility that they will need to be resent. As a result, there are fewer missed deadlines, which improves the timely transmission of important data messages. Last but not least, as was previously stated, the suggested approach achieves the lowest percentage of missed deadlines.