## 5 Performance Evaluation  
To evaluate our proposed EC-PSO algorithm, we compare it with some other works, namely VD-PSO and the algorithm [PERSON] et al. proposed. The experiment environment is Matlab simulator and 400 sensors are randomly placed in a \(1000\times 1000\) m\({}^{2}\) field. We set the initial energy of each sensor 0.5 J and some relevant parameters are described from Table 2.  
Figure 8 describes that with the number of rounds increasing, the total energy of the network increases simultaneously in different algorithms. VD-PSO only adopts rendezvous points for mobile agent to gather information and nodes far away from the rendezvous points will spend much energy for long-distance data transmission. Therefore, it consumes the most energy. Both the algorithm [PERSON] et al. proposed and EC-PSO introduce the clustering method to reduce the energy consumption and simplify the topology of the network. However, the algorithm [PERSON] et al. proposed doesn't consider the distribution of CHs and a few nodes still need to transmit their data to the forwarder by long distance communication. EC-PSO consumes less energy compare to the other two algorithms because it uses optimal distance communication to transmit data to the CHs with less hops and much energy are conserved from unnecessary forwarding. The CHs EC-PSO elects are also more reasonable than the other two algorithms. Lifetime is a crucial standard to evaluate the performance of network and it is usually defined as the time when the first node dies. We can clearly see from Figure 9 that our proposed EC-PSO has a better performance in terms of network lifetime. The first node dies around about 400 rounds for VD-PSO, and about 700 rounds for the algorithm [PERSON] et al. proposed. However, in EC-PSO, it can be about 900 rounds when the first node dies. Both VD-PSO and the al \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Network size (R) & \(1000\times 1000\) m\({}^{2}\) \\ Number of nodes (N) & 400 \\ Initial energy (\(E_{0}\)) & 0.5 J \\ Energy consumption on circuit (\(E_{dec}\)) & 50 nJ/bit \\ Free-space channel parameter (\(\epsilon_{fs}\)) & \(10\) pJ/bit/m\({}^{2}\) \\ Multi-path channel parameter (\(\epsilon_{mp}\)) & 0.0013 pJ/bit/m\({}^{4}\) \\ Packet length (\(l\)) & 1000 bits \\ Distance threshold (\(d_{0}\)) & \(\sqrt{\frac{\epsilon_{fs}}{\epsilon_{mp}}}\) m \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Simulation parameters. Figure 8: Energy consumption of the network. proposed do not consider the energy balance and they both consume more energy than EC-PSO. Additionally, EC-PSO uses protection mechanism to protect weak nodes and energy holes are avoided when transmitting. Therefore, EC-PSO achieves the obvious improvement in enhancing the lifetime of the network. First node dies (FND) is also an important evaluation criterion for network performance in WSNs. In Figure 10, we test different algorithms under the circumstances of different number of sensor nodes. Simulation result shows that when the number of sensor nodes rises, our algorithm still has a better performance than the other two algorithms because we take the energy balance into consideration. In order to demonstrate that the mobile data collector has improved the performance of the network, we conduct two different scenarios. One of the scenarios is that the sink is fixed at the center of the network and CHs transmit data to the sink using greedy algorithm. One CH chooses a closest CH towards the sink node as a relay node until the data is transmit to the sink. Another scenario is that we have mentioned in Section 4.7. Result of the simulation is shown as Figure 11. In Figure 11, we can clearly see that the round of first node die in scenario 1 is much earlier than that of scenario 2 because the static sink causes the hot spots problem and make nodes close to the sink premature death. However, the mobile data collector may result in packages loss in transmission because of its movement and unpredictability.