## 4 Performance Evaluation
### Experimental Results and Discussion
#### 4.3.1 The Impact of Network Size
This phenomenon is due to the fact that the packet traffic of EEABR scheme focuses on particular nodes that are close to the sink or  
Figure 11: Standard deviation for different WSNs with different sensor nodes. Figure 10: Minimum energy for different WSNs with different sensor nodes. Figure 9: Energy efficiency for different WSNs with different sensor nodes. located in the efficient route as outlined by its proactive method. In contrast, SensorAnt distributes the energy consumption over the whole WSN thereby achieving the required energy balance across the network. Accordingly, the sensor network life span is extended. Figure 14: Energy balanced with 100 Sensor nodes at 2,000 s for (**a**) SensorAnt and (**b**) EEABR. Figure 12: Throughput for different WSNs with different sensor nodes. Figure 13: Energy balanced of SensorAnt and EEABR for 50 sensor nodes at 2,000 s.  
Figure 15(a,b) illustrate the residual battery capacity of SensorAnt and EEABR respectively, after different periods of time. In Figure 15(a), it is observed that in the proposed SensorAnt, the energy residue is still balanced across the sensor nodes even if the battery depleted slowly and regularly with increasing run time. This is due to the self-adaptive scheme employed in the proposed SensorAnt which always monitors the sensors' remaining energies and only permits those sensor nodes with high residual energy to participate in the routing function. On the other hand, Figure 15(b) shows clearly that the sensor nodes in the EEABR scheme exhaust their residual energy very fast as the run time increases; which is due to the lack of an appropriate energy consumption distribution among the sensor nodes. Instead, the scheme only focuses on maintaining the efficient route at the expense of increased energy depletion. Figure 14: _Cont._  
Figure 8:0 Energy balanced for 50 sensors with different times for (**a**) SensorAnt and (**b**) EEABR. As a result, while SensorAnt attempts to maximize the uniformity; the traffic is scattered through the whole WSN leading to better energy balancing. This adequate balancing comes from the efficient way of exploiting the battery resources. Figure 16 shows the total energy consumption of the two schemes with time. Again, it can be clearly seen that the proposed SensorAnt presents a superior performance compared to the EEABR. Here, the energy consumption increases gradually with time when the proposed SensorAnt is employed. However, with the EEABR scheme, energy consumption increases rapidly with the time and sensor nodes increase. Figure 8:1 Total energy consumption for different networks under different time for SensorAnt and EEABR. Figure 8:2 _Cont._  
In these experiments, we observe that our SensorAnt shows better energy efficiency than EEABR. Increased number of nodes EEABR consumes energy faster, whereas SensorAnt proceeds to become more stable. This is because in our model, Ant-B find out paths to destination according to the pheromone trail connected with the current WSN energy state gathered by Ant-F. After a period of transmission, the remaining energy of sensor nodes are changed for different traffic, accordingly the WSN status will be affected, then a different route with better function quality will be selected to assist the following transmission during the path search stage. Additionally, when the traffic load from the sender to destination stays for some length of time and becomes heavier, sensor's residual energy in SensorAnt will be checked occasionally and if it found that the energy drains fast a new path searching operation will be started and new paths will be established that avoids over use of affected SNs.