## 4 Performance Evaluation
### Network Lifetime Prolongation and Coverage Preservation
It clearly indicates that PEGASIS, LEACH-Coverage-U and LEACH provide poor capabilities in maintaining the sensing coverage ratio. The proposed CoCMA maintains the sensing coverage ratio at 100% until the 2,000\({}^{\text{th}}\) round which doubles that provided by  
Figure 8: An illustration of uniform deployments of sensor nodes and POIs in the sample network and the simulation result after applying the CoCMA. The yellow star-shaped points represent POIs, and the blue circles represent the sensing ranges for nodes. Figure 9: Illustration of random deployments of sensor nodes and POIs in the sample network and the simulation result after applying the CoCMA. (Left) A random deployment of 100 sensor nodes and 100 POIs in a 50 m \(\times\) 50 m sensing field. (Right) The optimal schedule for nodes is yielded by the CoCMA. the PEGASIS. The proposed CoCMA and EGDG yielded similar results in the interval between the 2,000 th and the 3,500 th round, but the sensing coverage ratio of the EGDG method decreases to around 97% at the 2,000 th round. The advantage of the proposed CoCMA becomes significant starting from the 3500 th round. Moreover, the network lifetime is prolonged to 5,118 rounds by the CoCMA, which lasts about 1,000 rounds longer compared to the EGDG method. Figure 11 shows the number of rounds versus the percentage of dead nodes. The EGDG, PEGASIS, LEACH-Coverage-U, and LEACH methods lose their 50% of nodes at the 1950 th, 1120 th, 502 th, and 300 th round, respectively. The simulation result demonstrates that the proposed CoCMA significantly prolongs the lifetime of the network, even outperforms the EGDG method. We observe that the nodes die rapidly using PEGASIS, LEACH-Coverage-U and LEACH methods, since they do not have node-scheduling strategies. On the contrary, the EGDG and the proposed CoCMA can inactivate the redundant nodes via node-scheduling strategies that save much energy, so the longer network lifetime can be obtained. A longer network lifetime and higher sensing coverage are important for coverage-preserving sensor networks. The simulation results show that the CoCMA is able to achieve these missions. Thereby, the QoS of the network can be improved by using the CoCMA. Figure 11: The number of rounds versus percentage of dead nodes (%) for the uniform deployment scenario. Figure 10: Sensing coverage ratio versus the number of rounds in the uniform deployment scenario. In order to compute the energy distribution over the network, a 2-D Gaussian distribution is utilized as a point spread function to estimate the spatial strength of energy. Figure 12 depicts the energy distribution over the entire network at different rounds of the simulation. In CWSNs, cluster heads consume most energy since they are responsible for data collection, fusion and packet transmission with a sink. As a result, the energy density on a specific sensing area will decrease rapidly if a large number of nodes congregate at a sub-region of the sensing field as time passes. If the cluster is far away from the sink, the energy level of the cluster will also be lowered. Since the sink is located at (50, 200) (which is far from the sensing field) in this simulation case, the nodes located at the bottom of the given CWSN exhausted their energy rapidly because of the long distance between these nodes and sink. An inspection of Figure 12 indicates that most sensor nodes have died in the sensing field from the sub-figure of the 4,900\({}^{\text{th}}\) round except for the sensor nodes at the top of the sensing field. Clearly, the position of a sink will significantly affect the distribution of the remaining energy for the entire CWSN.