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Top1. Introduction
In recent years, with the development of wireless sensor network (WSN), there has been a new trend in the field of agriculture. The field of precision agriculture, such as remote observation, analysis and control management, is related to wireless sensor networks (Ojha, T., Misra, S., & Raghuwanshi, N. S. . 2015). Wireless sensor networks belong to a kind of important wireless networks. They are mainly composed of spatially distributed autonomous sensors, which are used to collect local data and realize mutual communication (Fubao, W., Long, S., Fengyuan, R., 2005; Wang, J., Ghosh, R. K., & Das, S. K., 2010). Sensor node positioning technology is one of the key supporting technologies in many applications of wireless sensor networks. It is an important foundation for technologies such as network topology management, coverage control, and inter-node routing algorithm design. Positioning information will affect the overall performance of the entire network. Therefore, how to effectively improve the positioning accuracy of sensor nodes has become a research hotspot in the field of precision agriculture.
According to whether the actual distance or angle between nodes is measured during the positioning process, the positioning algorithm is roughly divided into a distance-based positioning algorithm and a distance-independent positioning algorithm (Woo, H., Lee, S., & Lee, C. . 2013). Distance-independent algorithms do not require additional hardware configuration and can be located only based on network connectivity (L Yun., J Ming., & C Cheng.,2009) . Therefore, the distance-independent algorithm has the advantages of strong anti-interference and low hardware cost. Typical distance-independent positioning algorithms include centroid algorithm (A Xun., J Ting., Z Zheng., 2007), convex programming localization algorithm (Z Han., L Feng., 2007), and DV-Hop algorithm (B Feng., J Xiao., &M Hui., 2010) .
The DV-Hop algorithm generally uses the least squares (LS) principle to estimate the coordinates of an unknown node. Although it can reduce the positioning error to a certain extent, the LS algorithm is susceptible to the cumulative ranging error, and the solution speed is slow, which affects the positioning accuracy. Therefore, many researchers consider introducing swarm intelligence optimization algorithm to improve the positioning accuracy (H Zhong., L Jing.,2008). For example, the literature (Y Rong., 6Z Ling.,2011)combines the particle swarm algorithm with the ant colony algorithm for post-optimization. The unknown node position is estimated in the third stage of the DV-Hop algorithm to achieve a minimum distance or minimum error. Literature (W Jin., L Xu., & W Min., 2007) established a mathematical model with unknown node locations as parameters. In addition, genetic algorithm is used to solve the optimal position and improve the positioning accuracy. According to the criterion of anchor node selection, the literature (O Yang., H Jin., & B Hong., 2011) uses the average of the previous generation node and the contemporary node position as the reference node of the next generation target node, thereby reducing the influence of the ranging error. Therefore, an improved particle swarm optimization algorithm is used to optimize the positioning results. In literature (W Ya., Y Jian.,2014), by setting the corresponding constraint fitness function and distance fitness function, the search quantity during positioning is reduced and the convergence speed is accelerated. The introduction of swarm intelligence optimization algorithm improves the accuracy of positioning to a certain extent (Z Shi., S Mei., &T Yi.,2009) .