摘要
针对传统的农业物联网路由以及网关选址时,考虑实际地形对传输损耗的影响不够,导致节点电能的浪费以及维护费用增加的问题,本文首先对农场地形以及已布置终端的位置进行建模,使用K-means算法确定路由初始位置以及该路由负责对接的终端。在考虑电磁波自由空间损耗以及绕射损耗的前提下设计合理的适应度函数,基于一种可变惯性系数的粒子群优化算法对路由和网关的位置进行优化。最终模型给出最大的电磁波损耗数据,用于在确定节点的最大发射功率时提供参考。算例仿真发现,路由位置通过PSO算法寻优,最大传输损耗最多可降低27.82%。实地检验发现,本算法所选取的最优点通信质量显著高于其附近的点,RSSI提升达12%~20%。模型最终给出的路由和网关最大传输损耗与最优布局位置对于实际节点铺设具有指导性意义。
The node location of the agricultural IoT node is of great significance for reducing power consumption.However,the existing literature rarely considers the loss in the signal transmission process in the node location problem,especially the diffraction loss caused by the terrain factor.A method of node location based on K-means and PSO algorithm was proposed.Firstly,the K-means algorithm was used to determine the approximate location of each route and the terminal which were responsible for docking according to the distance of the straight line.Then,considering the electromagnetic wave free space loss and diffraction loss,combined with the modeling of the nodal topography,using the Fresnel integral and free space loss formula,a fitness function was constructed.A variable inertia coefficient PSO algorithm was used to solve this function.This method increased the inertia weight factor to improve the particle search ability when the global optimal point was updated.Anyway,the inertia to accelerate the convergence of the algorithm was reduced.The improved PSO algorithm was used to optimize the location of routers and gateways.The simulation found that the routing position was optimized through the PSO algorithm,which can reduce the maximum transmission loss by up to 27.82%.Field inspection showed that the optimal communication quality selected by this algorithm was significantly higher than that of the nearby points,and the RSSI was improved by as much as 12%to 20%.In addition,the model gave the maximum electromagnetic wave loss data,which can be used to determine the maximum transmission power of the node and estimate the energy loss of the node,so as to make a more rational estimate of the overall energy consumption of the node,and effectively reduced the subjectivity and arbitrariness of nodes planning.
作者
谢家兴
梁高天
高鹏
王卫星
XIE Jiaxing;LIANG Gaotian;GAO Peng;WANG Weixing(College of Electronic Engineering,South China Agricultural University,Guangzhou 510642,China;Guangdong Modern Agricultural Science and Technology Innovation Center for Intelligent Orchard,Guangzhou 510642,China;Guangdong Engineering Research Center for Monitoring Agricultural Information,Guangzhou 510642,China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2022年第5期275-281,共7页
Transactions of the Chinese Society for Agricultural Machinery
基金
广东省科技专项资金项目(2020020103)
广东省教育厅特色创新类项目(2019KTSCX013)
广东省现代农业产业技术体系创新团队建设专项资金项目(2022KJ108)
省级乡村振兴战略专项省级组织实施项目(粤财农〔2021〕37号)
广东省科技创新战略(“攀登计划”)专项资金项目(pdjh2021b0077)
财政部和农业农村部:国家现代农业产业技术体系项目(CARS3214)。
关键词
农业物联网
节点选址
K-MEANS算法
粒子群优化算法
绕射损耗
自由空间损耗
agricultural internet of things
node location
K-means algorithm
particle swarm optimization algorithm
diffraction loss
free space loss