## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.3.1 The Impact of Network Size  
Several experiments results have been conducted to verify the impact of the network size. Figure 7 shows the comparison of the proposed SensorAnt with the EEBAR model in terms of total energy consumption. Here, our SensorAnt shows better performance in terms of energy conservation compared to the EEBAR algorithm. As can be seen, the total energy consumption for SensorAnt are smaller than EEBAR with different SNs. This is because SensorAnt can optimize the energy usage of SNs inside WSN and explore the better path based on both function qualities used in our model. The SNs in SensorAnt always monitor their energy level to avoid those SNs with least energy from being \begin{table}
\begin{tabular}{l l} \hline
**Name** & **Value** \\ \hline Simulation Tool & QualNet V5 \\ Channel Frequency & 2.4 GHz \\ Traffic & CBR \\ Packet Size & 32 byte \\ Interval Time & 1 second \\ Energy Model & Micaz \\ Data Rate & 250 kbps \\ Initial Energy & 1,250 mJ \\ \hline \end{tabular}
\end{table} 
Table 1: Simulation setting. used for an extended period of time. Clearly, EEABR exhausted much more energy at large number of sensor nodes. These prove that SensorAnt is an efficient mechanism to decrease the energy usage. Figure 8 illustrates the average energy of all sensor nodes at the end of the simulation. Clearly, energy depletion rate in SensorAnt is fairly constant over the various nodes density. The reason is that SensorAnt employs balanced scheme and equalizes the energy of sensor nodes throughout the system life time. Figure 9 shows the performance of energy efficiency in SensorAnt compared with EEABR; which is defined as the ratio between the whole energy consumption and the total of messages reached at the sink. It can be observed that the energy efficiency of our SensorAnt far outperforms EEABR; this is because SensorAnt receives more packets with the lowest energy-cost than EEABR. This stems from the fact that our model minimizes the number of dropped packets by preventing congestions hence this limits the number of retransmissions. Figure 10 shows the lowest energy among all sensor nodes. As can be seen our SensorAnt gives the best result compare with EEABR; this is because in WSN most of the nodes play the role as routers, and data forwarding which consumes much energy, whereas our SensorAnt uses multiple alternative routes that have best qualities. Figure 11 shows the standard deviation of SensorAnt and EEABR. As can be seen SensorAnt gave the smallest changes; this is because it utilizes the sensor nodes most fairly. This implies that, if the number of communications increases, the energy of sensor nod's inside the WSN can be balanced better. Figure 8: Average energy for different WSNs with different sensor nodes. Figure 7: Total energy consumption for different WSNs with different sensor nodes. As can be seen in Figure 12 our SensorAnt performs much better than EEABR in terms of throughput; this is because our model prevents congestion when the traffic load becomes heavier by distributing it over multiple routes. This is done in our proposed method using a proactive mechanism during the period of data transmission from the sender to the sink which periodically checks the energy status of WSN and selects the path with a better function quality by assigning more pheromone to it through positive reinforcement while the paths with little energy will get negative reinforcement. Figure 13 and Figure 14(a,b) show the energy balance plots of both algorithms. As can be seen, SensorAnt attains the more preferred energy balance in both the 50 and 100 sensor networks compared to the EEABR scheme over the 2,000 s period. Whereas the sensors in EEABR drained their battery energy rapidly, almost all sensor nodes in SensorAnt still have a high battery capacity which allows them to live longer and keep responding to the occurring events.