## 1 Introduction
Among them, the routing protocol based on the clustering mechanism is used comparatively more than others [9]. In a monitored environment with a large-scale deployment of sensor nodes, the monitored properties (such as temperature, humidity, light, wind and precipitation) are usually distributed \"structurally\" with greater redundancy. Let us consider for example the precipitation distribution of the Taihu Lake Basin, the branch river system in the downstream region of the Yangzi river, centered at Taihu Lake with the drainage channel of the Huangpu river. The river system occupies an area of 36,500 km\({}^{2}\), including Tianmu Mountain in Zhejiang Province and Maoshan Mountain in Jiangsu Province on the west, the end of Yangzi River on the north, facing the East China Sea and Hangzhou Bay on the east and south respectively. The precipitation in the west part of Zhejiang Province (Zhexi, in short) is higher than other areas no matter what year. The precipitation in Pudong and Puxi of Shanghai (a city located at the Coast of East Sea) and Yangcheng Dianmao area of Suzhou City is within the low value area of the precipitation line in every year. That is due to the impact of orographic lift on the precipitation. Zhexi is the only mountain area in the basin while Yangcheng Dianmao is a well-known low-lying land. The spatial distribution of the precipitation in the basin is that it is large in the former and small in the latter. This illustrates the impact of orographic lift on the precipitation. According to years of observation data, the general trend of the precipitation is that the upstream is larger than the downstream, the west than the east, the south than the north and the mountain than the plain in terms of space [10]. However, from the existing general pattern of precipitation information extraction, it can be seen that the system-level precipitation information processing technology is essentially a background  
Figure 1: WSN Nodes for environmental monitoring. approach of information processing that distributes measuring nodes based on geographical characteristics and processes data centrally. Although the information can be converged after all the data are sent separately to the background, the results obtained from this method are usually not as accurate as those obtained from the fusion in the front, and sometimes even produce fusion error. The participation of local information from the data source is generally required in information fusion, such as time and location of data was generated, and the location generating the data. The clustering mechanism-based routing protocol [9] demonstrates its superiority. Its idea is that in a multi-hop communications process, a cluster head is used in data fusion to reduce the amount of data sent to sink nodes and to achieve the purpose of efficient use of energy. Compared with the flat routing protocols (flooding, gossiping, _etc._), the introduction of the clustering mechanism improves performance significantly for similar or dissimilar sensor fusion of data collection with high correlation (or high similarity). An earlier proposed one is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [11,12] (referred as the traditional LEACH protocol in this paper). This idea of hierarchy-based protocol design has been accepted by more and more scholars. However there are still some problems in the practice of this routing protocol, the most important of which concerns the unreasonable selection of cluster heads, such as uneven distribution, no regard to the remaining energy of nodes, _etc._ Some improved algorithms have been proposed to solve these problems [13-15]. However, most of them select cluster heads according to the status information of candidate nodes without considering the information of their neighbors, which tends to result in blind nodes inside clusters, possibly reducing the survival time of the network.