## 4 Experiment and Result Analysis
### Results and Analysis  
In the simulation, we used as independent variables the size of data and mobile agents, target numbers, and number of nodes in the network. Dependent variables were execution time and energy consumption. In Figure 16, we have a fixed number of sensor nodes, the mobile agent has a fixed size of 1 kb and the data size changes from 1 kb to 50 kb. Since the proposed algorithm and LCF (local search) are based on mobile agents, and mobile agents do not change size, the ratio of data size and mobile agent size has no effect on execution time. We do see that with transmission of data over 23 kb, in terms of execution time, the C/S model performs worse than the mobile agent algorithms (LCF and proposed). The proposed algorithm is marginally faster than the LCF algorithm.  
The same is true for energy consumption, as shown in Figure 17. Again, the C/S model quickly slows down with data size, and the proposed algorithm is slightly f  
Figure 16: Execution time.  
Figure 18 shows the network deployment under increasing number of targets. The amount of data for processing also increases, and therefore the energy consumption. The C/S model has a significant increase in energy consumption as the number of targets increases, and the LCF and proposed algorithms are more efficient. Between the two, the proposed algorithm still uses less energy than LCF.  
Figure 19 shows the network delay with increasing number of targets. The LCF algorithm has the largest delay overall. The proposed algorithm and C/S have similar delays, with C/S slightly slower. In the C/S model, agents transmit the data collected by the sensor nodes around the target, which decreases delay time. The amount of data aggregation to the processing nodes increases with the number of targets. At some point, the processing node in the C/S model becomes the bottleneck, and the data delay increases. The algorithms based on mobile agents have a small extra overhead for small numbers of targets. When the volume of data increases, multi mobile agents migrate constantly between sensor nodes, and transmit only the final fusion results, so the fused data is smaller, and the delay time is shorter.  
Figure 17: Energy consumption.  
Figure 18: Energy consumption for different target numbers.  
In Figure 20, we increase the number of nodes and measure energy consumption. When the node density is small, the node distribution is wider, the correlation between nodes is smaller, and data fusion to reduce the amount of data has less effect. The transmission energy gain cannot offset the energy consumed by the mobile agent itself. With an increase of the number of nodes, the correlation between the data improves, and the energy gain of the mobile agent is much higher than the energy consumed by itself. The energy consumption based on the C/S model is obviously higher than that of the mobile agent. Transmission of raw data uses most energy here.  
In Figure 21, we measure the number of hops as the number of nodes increases. The graphs are like the graph showing energy consumption, since transmissions consume most energy. Since the C/S model transfers non-fused data, the total number of transmissions is much larger. Between the two agents, the proposed algorithm has fewer transmissions, since it stops collecting data from the sensor nodes once the threshold has been reached.  
Figure 19: Delay for different target numbers.  
Figure 20: Effect of node density on energy consumption.  
Figure 22 shows the increase in number of links as nodes move faster and move in and out of range. From Section 3.3, the new link expected arrival rate is proportional to the density of nodes in the network, and the energy of the signal transmission radius of sensor nodes is proportional to the node, P represents the average number of nodes in a transmission area, the value of each parameter is the minimum speed of 0 m/s, the maximum speed of 40 m/s, the transmission radius R of 250 m.