# CoDA: Collaborative Data Aggregation in Emerging Sensor Networks Using Bio-Level Voronoi Diagrams
## 5 Simulation Results
Figure 7(a) shows the variation of coverage rate. It can be seen that the stability of DBB and CoDA is relatively better than HCDD, and they can maintain a high coverage rate. When the number of nodes is small, the coverage rate of HCDD can be maintained at a stable level. However, with the increase of the number of nodes, the coverage rate fluctuates greatly, which shows that the algorithm is greatly affected by the node's transmission radius. Figure 7(b) shows the corresponding number of relay nodes. We can find that the deployment of relay nodes increases rapidly and thus achieves a higher coverage rate in DDB while the node density increases. In other words, the coverage rate of DDB is implemented by generating more relay nodes. It will cause too much redundancy of relay nodes and may result in serious communication interference. The number of relay nodes in CoDA is relatively stable at this point and indicates the reasonable deployment of relay nodes and topology control can realize the high coverage. Further, we analyze the data collecting path length. Because those protocols are all applied with multi-hop transmission, we do not constrain the communication radius of relay nodes in the experiments. Then, the communication radius are adaptively adjusted in each protocol using the most energy-efficient way, and average hop count is statistically evaluated. As shown in Figure 9, we can see the increase of the number of nodes, the hop corresponding growth trends. While the node number reaches 1000, the path hops of HCDD has increased more than 10. As a consequence, this inevitably leads to high network latency and it can't be better adaptive to the delay-sensitive applications with HCDD protocol. On the other hand, the basic hops of CoDA protocol for data acquisition can always keep within five hops. In conclusion, the cluster head can forward packets to the relay node via other cluster heads and this reduces the number of forwarding steps, which may benefit from the relay nodes of reasonable deployment, reasonable planning for the lower level Voronoi diagram, and the application of particle swarm optimization. Figure 8: Comparison of relationship between the coverage rate and the number of relay nodes. In order to analyze the equilibrium characteristics of the node average energy consumption, we deploy 200 nodes with unique identification numbers and analyze the average energy consumption of these nodes in 100 rounds [51; 52]. The average energy consumption of nodes with different number of sensors is shown in Figure 10. From the result, we can see that the node's energy consumption of CoDA is more balanced rather than the HCDD and DDB. This is because the CoDA can manage the nodes hierarchically via double Voronoi diagram. Meanwhile, the CoDA can determine the cluster heads via the optimal clustering method. As a result, the node average energy consumption remains at a lower level. At the same time, the cluster heads can also balance well the energy consumption of different cluster heads in multi-hop transmission mode through the optimized path forwarding to relay nodes, which can avoid the overloaded energy consumption. The energy consumption of the each node is shown in Figure 11, and the energy consumption of the top 20 clusters is more comparable to the latter. The energy consumption in HCDD and CoDA is more smooth and lower than in DDB. The DDB protocol adopts the polling mode method to select cluster heads with same probability, and there may be a chance that a cluster head will die earlier and cause the hot-spot problem. The HCDD and CoDA algorithms in the cluster head selection are conditional and relatively stable. By searching for the solution for minimizing the maximum distance between adjacent nodes and the maximum number of hops simultaneously, CoDA obtains the optimal path for cluster heads to deal with inter-cluster communication. Therefore, it has a better effect on energy consumption. Figure 10: Comparison of average energy consumption of sensor nodes. Figure 9: Comparison of average path length.