## 5 Simulation Verification
### Measurement and Selection of Control Parameter \(\alpha\)  
In Section 4.2.1, the directional source grouping algorithm can adjust the size of the PE region by controlling parameters, and it can adjust the number of MA. Therefore, in the simulation, the influence of the \(\alpha\) value on the performance of the proposed algorithm should be examined first. A scenario of 15 source nodes is set up, and a large number of random seeds are used to simulate the impact of energy, delay and the influence of EDP with the change of \(\alpha\).  
As can be seen from Figure 11a,b, when \(\alpha\) is less than 0.5, the energy consumption in the network remains at a high level, while the task delay is the smallest. This is because the distance between the source node and the data center occupies an excessive proportion in the weight calculation of undirected connected graphs. This extreme condition results in all source nodes to be connected to the data center in the construction of the minimum spanning tree. That is, after the multi-mobile agent marshalling, a single mobile agent is assigned for each source node by the data center. Obviously, task latency can be minimized at this time. However, due to the fact that the advantages of data fusion of  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Value** & **Parameter** & **Value** \\ \hline Raw data size & 2048 bits & Network size & \(1000\times 500\) m \\ MA code size & 1024 bits & Radio transmission range & 60 m \\ MA accessing delay & 10 ms & Number of sensor node & 800 \\ Data processing rate & 100 Mbps & Media Access Control (MAC) layer standard & 802.11b \\ Aggregation ratio & 0.8 & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Setting of simulation parameters.  
mobile agents have not been fully exploited, the energy consumption of mobile agents is maintained at a high level. It is worth noting that when the control parameter \(\alpha\) is greater than 0.5, the energy consumption in the network is decreased gradually with the increase of trade-off factor, while the delay of tasks is increased gradually. This trend tends to flatten when the control parameter \(\alpha\) approaches 1.0. The curve trend in the graph fully illustrates the inverse relationship between energy consumption and task delay. Thus, the required multi-mobile agent travel planning algorithm is obtained by adjusting the control parameter \(\alpha\).  
In the last section, energy delay product EDP is proposed to be taken as a comprehensive performance index. Therefore, the corresponding relationship between EDP and control parameter \(\alpha\) in the experiment need to be examined. Obviously, the relationship between energy consumption and task delay is increasing and decreasing respectively. There must be an optimal solution for the EDP value, which is the reflection of their comprehensive performance. From the curve of Figure 11c, it can be seen that, when the value of control parameter \(\alpha\) is less than 0.5, the performance of EDP remains unchanged; this is directly related to the stability of energy consumption and task delay in this range. When the control parameters \(\alpha\) continue to increase to 0.6, the EDP values continue to decrease. This means that the comprehensive performance of the algorithm has been continuously improved. However, when the control parameter \(\alpha\) continues to increase to 1.0, the EDP value increases; this means a drop in the performance of the algorithm. Therefore, when the control parameter \(\alpha\) is 0.6, the proposed algorithm can achieve the best comprehensive performance of EDP.