# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 6 Performance Evaluations
### Simulation Results  
We compare the network lifetime, miss ratio, end-to-end latency, packet delivery ratio, and energy imbalance factor of our approach to those of [18; 19; 22; 24] in order to assess its feasibility and effectiveness. In contrast to the earlier research in [18; 19; 20; 21; 22; 23; 24], all subsequent experiments are carried out with two scenarios since our solution takes the environmental  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Node deployment strategy & Uniformly random \\ Number of sensor nodes & 200 \\ Maximum retransmissions number & 4 \\ Packet size & 50 bytes \\ Buffer size & 128 bytes \\ Frequency & 868 MHz \\ Path loss exponent & 3 \\ Transmission power & 0 dBm \\ Initial energy of nodes & 125 mJ \\ Noise floor & \(-\)115 dBm \\ Maximum radio range & 150 m \\ Data rate & 20 Kbps \\ Shadow fading variance & 3 \\ Reference distance & 1 m \\ Event area (Wildfire) & Circles with a radius from 5 m and 50 m \\ Normal range for temperature & [0, 50] \({}^{\circ}\)C \\ Normal range for relative humidity & [30\%, 80\%] \\ Extreme range (temperature) & [\(-\)10, 100] \({}^{\circ}\)C \\ Extreme range (relative humidity) & [0\%, 100\%] \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation environment parameters.  
effect into account as one of the key criteria in both the route determination of the mobile sink and the routing mechanism of the urgent data.  
1. First scenario: It is considered that environmental statistics are within the normal range; therefore, environmental influences do not affect network performance. It demonstrates the performance of the approaches under normal environmental conditions;
2. Second scenario: It is assumed that the environmental data are outside the normal range. It demonstrates the performance of the approaches under environmental conditions outside the normal range. In the second scenario, all following studies assume the network is impacted by wildfire. Environmental events are considered to occur 400 seconds after network start up.  
Additionally, as previously mentioned, none of the earlier studies in [18; 19; 20; 21; 22; 23; 24] take into account the real-time transmission of urgent data packets, while the data from the cluster heads are acquired as the mobile sink approaches each cluster head. As a result, without the usage of a routing protocol, a comparison with the earlier research published in [18; 19; 22; 24] would not be fair. As a result, the suggested routing method is applied with all the earlier research from [18; 19; 22; 24] in all subsequent experiments. The suggested routing algorithm must also be assessed concurrently. As a result, it has to be evaluated against alternative routing techniques. In all subsequent experiments, a different routing method from [28] that is more similar to our suggested approach is taken into account.  
As a result, two scenarios are taken into consideration for each situation in order to demonstrate the effectiveness of the proposed approach:  
1. Case 1: It exhibits the performance of the techniques using the proposed routing algorithm for urgent data collection;
2. Case 2: It demonstrates the performance of the approaches with the PSO-based routing approach in [28] for urgent data collection.