## 5 Simulation and Results Analysis  
To verify the validity of the proposed scheme, we will compare the performance of CPCSS, Energy Aware Evolutionary Routing (EAER) [53], and Energy Efficient Heterogeneous Clustered (EEHC) [54]. We set up the model and experimental environment, and each examination was run 10 times to obtain the average results so as to eliminate the error caused by randomness. In order to implement senses' classification, we adopted the data sets from the Intel Berkeley Research Lab [55], where actual sensor nodes are deployed in the lab, during the data sensing process. The data collected by sensor nodes includes the humidity, temperature, light, and voltage values and so on. We randomly choose part of the nodes' temperature, humidity, and light data, and attach to all the sensors in the experiment, and generate the predicted data sequence for our tests. There are 200 sensor nodes deployed randomly in a 100 \(\times\) 100 2D plane, and the node's sensing range can make adaptive regulation in the range of 9 m to 12 m. Figure 6a,b indicates the active node distribution and active node coverage after the initial deployment of the network, in which the solid points are represented as active nodes in the network. From the nodes' deployment, it can be observed that most parts of the area are covered by more than one sensor node. Moreover, lots of redundant nodes will lead to produce overload data and mass collision in wireless channel. Intuitively, if those sensor nodes are off-duty when they have no sensing or relaying work to do, the whole system will can achieve a better performance in aspects of energy consumption. With the operation of the network, the node scheduling scheme determines redundant nodes gradually. Figure 7a,b shows the distribution and coverage of active nodes after sleep scheduling. The sign \"\(\alpha\)\" in Figure 7 represents the dormant node, and the number of dormant nodes arrives 157, which is about 78.5% of the total number. It can be seen that a large number of redundant nodes are scheduling into sleep state, and active nodes can still ensure the full coverage of the monitoring area, which can greatly save network energy overhead. Figure 6: The initial deployment of nodes. (**a**) Nodes’ deployment; (**b**) Coverage area. With the gradual depletion of the energy of some nodes, Figure 8 shows the situation while some active nodes failed in the network, where \"x\" indicates the failed node. During the current round, there are five active nodes being labeled failed state due to energy depletion, and the network can no longer be able to achieve full coverage of the region. Figure 9 shows that the relevant dormant nodes will be awaken to repair coverage holes. It can be seen that there are four dormant nodes being awakened, and the network retains high target area coverage, it can be observed that the CPCSS can effectively control the working status of nodes according to the network variation, so as to achieve the goal of saving energy consumption. In addition, the adjacent nodes in sleep state can be waken up timely to maintain high coverage of the system as soon as the appearance of some nodes failed due to energy depletion. Figure 8: The distribution and coverage while some active nodes failed. (**a**) Some nodes being depleted; (**b**) Coverage hole. Figure 7: The distribution and coverage of active nodes after sleep scheduling. (**a**) Dormant nodes’ deployment; (**b**) Coverage area. Figure 9: The distribution and coverage after some hibernated node being awaken. (**a**) Some hibernated node being awaken; (**b**) Coverage area. Figure 10 shows the coverage of the algorithm over rounds. For EEHC, if not carrying out any scheduling scheme, that is to say, all nodes are active and it can get high network coverage at the beginning of system running. However, with the increase of the number of rounds, the coverage will demonstrate a sharp drop.