## 5 Performance Evaluation
### Experimental Setup  
For the simulation experiments, algorithms including DHCO, LEACH, HEED, GASONeC, and DCFR ran on diverse deployment scenarios, for which sensor nodes were randomly distributed and the BS was located at a certain position. For example, in Figure 2, the simulation experiments had a deployment scenario of 100 sensor nodes randomly distributed in a \(100\)\(\mathrm{m}\times 100\)\(\mathrm{m}\) square, with the BS located at the center of the sensing field (coordinate (50, 50)). Moreover, diverse simulation experiments of DHCO, LEACH, HEED, GASONeC, and DCFR algorithms were implemented in MATLAB 2015b, and were conducted on a computer with an Intel Core i7-4790 CPU (Lenovo, Beijing, China) of 3.60 GHz and 8G memory, and a Windows 7 operating system. In general, the radio wave propagation was highly variable and difficult to be modeled. Thus, as for the radio model used in [23], our radio model was also a simplified model, in which the parameters that were used for computations in the simulation experiments refer to [21; 22; 25] and are listed in Table 1.  
Node Energy Consumption and Wireless Sensor Network Longevity for the Dynamic Hierarchical Protocol Based on Combinatorial Optimization Algorithm  
In this subsection, simulation experiments were conducted to compare the percentage of live sensor nodes of the DHCO algorithm with that of LEACH, HEED, GASONeC, and DCFR algorithms for different transmission rounds in WSNs. The sensing field was set to a \(100\) m \(\times\)\(100\) m square region and the BS was located at coordinate (50, 50); this was used to measure two different deployment scenarios separately: 100 sensor nodes with a random distribution and 200 sensor nodes with a random distribution. For each transmission round, every sensor node acted as a transmitter or a router, and consumed energy. In the DHCO algorithm, the statistical average of the nodes' energy consumption for one transmission round was calculated, and is depicted in Figure 3; the energy consumption of the BS \(v_{0}\) was extremely large, which resulted from the fact that the BS is responsible for receiving information about all sensor nodes, and each sensor node is only responsible for sending or receiving information about itself or several of its nearby neighboring sensor nodes. We found that the energy consumptions for all sensor nodes were close, owning to the balanced energy consumption via the DHCO algorithm. As the transmission round of the WSNs went on, the simulation experiments presented illustrative numerical results to assess the performances of the WSN routing algorithms, and these are shown in Figure 4.  
Figure 2: One-hundred sensor nodes’ random distribution in the deployment scenario of a \(100\) m \(\times\)\(100\) m square region, and the coordinate (50, 50) of the BS. Figure 4 indicates that, for the same transmission rounds, the curve of the DHCO algorithm corresponding to the percentage of live sensor nodes evidently always overtops that of the LEACH, HEED, GASONeC, and DCFR algorithms, and the number of sensor nodes in the WSNs has an effect on the transmission rounds. Hence, the DHCO algorithm shows better performance compared to the LEACH, HEED, GASONeC, and DCFR algorithms. This can be explained by the fact that the route with the maximal transmission capacity, being limited by the sensor node with the minimal residual energy rather than the better sensor node (i.e., the node with a greater node residual energy or shorter distance to its neighbors), is the selected object. Thus, the optimal route, holding sensor nodes with greater residual energy, has more chance to be chosen, and sensor nodes with low residual energy are protected, which can evenly distribute the network traffic load to all sensor nodes and improve the WSN longevity.