温室温湿度传感网簇头选举算法研究

Cluster Head Selection Algorithm for Temperature and Humidity Sensor Network in Greenhouse

针对目前传统温室当中仍然存在着远程操作困难、数据采集需要人工干预、智慧化程度低等问题,构建了以边缘计算为基础的智慧化边缘Mesh传感网,并提出一种基于改进LEACH算法的温室温湿度传感网簇头选举方法。利用ESP8266-12F型无线模块、NodeMCU型物联网扩展板和DHT-11型温湿度传感器组建了温湿度传感节点并开发了自动化数据采集算法,基于ESP8266-12F型无线模块构建了边缘无线Mesh传感网,实现了节点之间的自动化组网。针对中心路由器多节点负载消耗大以及信号传输速率低等问题提出了一种基于中心路由器RSSI值划分网络的方法,提升了网络的传输速率,在此基础之上改进了LEACH算法,设计了一种适用于温室温湿度Mesh传感网的Sink簇头选举方法,以均衡网络整体的能量效率。仿真结果表明,当初始簇头概率为0.1时,原始LEACH算法在中心位置出现簇头的概率为10.86%;应用权重k=1改进LEACH算法在中心位置出现簇头的概率为17.42%;应用权重k=2改进LEACH算法在中心位置出现簇头的概率为24.96%。

In view of the existing problems in traditional greenhouse,such as difficulties in remote operations,necessity of manual intervention in data collections,and low intelligence in production,an intelligent edge Mesh sensor network was constructed based on edge computing,and a cluster head selection method for temperature and humidity sensor network in greenhouse was proposed based on improved LEACH algorithm.The temperature and humidity sensor nodes were constructed by using ESP8266-12 F wireless module,NodeMCU type based Internet of Things extension board and DHT-11 temperature and humidity sensor,and the automated data acquisition algorithm was developed.Based on the ESP8266-12 F wireless module,the edge wireless Mesh sensor network was constructed,and the automatic networking between nodes was achieved.Aiming at the problems of large load consumption and low signal transmission rate of central router,a method of dividing the network based on RSSI value of central router was proposed,which improved the transmission rate of the network.A temperature and humidity sensor network Sink cluster head selection algorithm was proposed based on LEACH algorithm,which was suitable for the plant factory temperature and humidity sensor network deployment and beneficial to balance the overall energy efficiency of the network.The simulation experiment results showed that when the probability of the initial cluster head was set to be 0.1,by using the original LEACH algorithm,the probability was 10.86%for the cluster head appearing in the center of the sensor network;however,it was increased to 17.42%when using the improved LEACH algorithm with weight k=1;and the probability would be further increased to 24.96%for the cluster head appearing in the center position when using the improved LEACH algorithm with weight k=2.