## 5 Conclusions  
Aimed at the different coverage requirements of regional monitoring target points in real-world applications, this paper proposes a node deployment strategy based on twofold coverage for all coverage requirements of two in the region. Then, from the network coverage, this paper proposes a series of definition formulas for information integrity, validity, and redundancy. In the CH selection, this paper fully considers the uniformity of the CH distribution and proposes a strategy of dividing the monitoring area into four small cells with the BS as the center and then selecting the CHs in their respective cells. Finally, combined with the actual requirements of the WSN, this paper optimizes the Dijkstra algorithm, including (1) nonessential paths neglecting considerations and (2) a simultaneous introduction of end-to-end weights and path weights. Then, the optimized Dijkstra algorithm is applied to the establishment of the ICRM.  
The simulation results show that, compared with the random node distribution strategy, the uniform node distribution strategy, and the onefold coverage-based node deployment strategy in a cellular network, the node deployment strategy in this paper achieves relatively high coverage, information integrity, and validity, as well as a relatively low information redundancy. At the same time, compared with LEACH, DEEC, and GSEN, EECRP-HQSND-ICRM can reduce and balance network energy consumption, thus extending the network lifetime and improving the network throughput. It is noted that the CH selection mechanism of partition based on the uniformity of the CH distribution in this paper is mainly responsible for selecting CHs with large residual energy and uniform distribution, thereby effectively balancing network energy consumption and delaying the number of rounds until the network performance begins to decline. The ICRM is mainly responsible for finding the optimal transmission path from each CH to the BS in the network and preventing some CHs from dying early because of the long transmission distances from the BS, thereby further reducing network energy consumption and realizing more rounds of network data iterations, which has great significance for engineering. However, there are some shortcomings in our protocol:  
First, we used the 0-1 perception model for node deployment and QoS formula derivation. In actual situations, the perception of nodes is very complicated, and it is much less ideal than the 0-1 perception model. Therefore, we may later consider a node deployment scheme and QoS formulas based on a more complex perceptual model.  
Finally, the protocol is applicable only to 2D scenarios. Typical 3D scenarios, such as underground coal mines, underground pipe corridors, and indoor homes require the WSN to be arranged in three  
Figure 22: The comparison of the final throughput in the five protocols.  
dimensions. Therefore, in the future, we will consider proposing a clustering routing protocol suitable for 3D scenarios based on this protocol.  
**Author Contributions:** [PERSON] and [PERSON] conducted the research and performed the simulations. [PERSON] led the research. [PERSON], [PERSON] and [PERSON] supervised the work and suggested revisions. All authors participated in the writing of the article.  
**Funding:** This research was supported by National Natural Science Foundation of China (Grant No. U1709212), the Zhejiang Province Public Welfare Technology Application Research Project (Grant No. GF18F030010), and the Smart City Collaborative Innovation Center of Zhejiang Province. This work was also supported by the Zhejiang Provincial Key Lab of Equipment Electronics.  
**Conflicts of Interest:** The authors declare no conflict of interest.