The RSSI Optimization Algorithm Based On ZigBee Node

Wireless sensor network(WSN)is a kind of distributed network from simple star network to multi-hop wireless network,which consists of several to several hundred or even thousands of nodes.These sensor nodes have the advantages of small size and low cost,and are widely used in monitoring systems.Today,this network is used in industrial and consumer applications and is also used in railway landslide monitoring systems.However,these applications need to know that node location information is valuable.Therefore,positioning in the WSN occupies an important position,is an active research field.At present,the localization problem in WSN has attracted the attention of researchers.Most researchers have studied the distribution of nodes in a planar environment and achieved high positioning accuracy.However,nodes in some special occasions are also distributed in the outdoor slope environment,although the application of this environment less But in some cases it has a very important significance.This paper considers the situation of the nodes deployed in the landslide monitoring system along the railway,and uses the distance change between the nodes to judge the occurrence of the landslide hazard,so as to inform the dispatching center to react accordingly to ensure the train and personnel Safety.The GPS location isusually used in the outdoor environment to obtain the node location,but if the landslide monitoring system on the node all configured GPS cost is expensive.In order to reduce the cost,this paper uses the signal strength between nodes to estimate the distance between nodes,and through the positioning algorithm to determine the node in the structure of the two-dimensional coordinate system position.In this paper,the plane and slope of the two environments are considered to estimate the distance between nodes,which aims to study the influence of external environmental factors on localization accuracy.And two methods are employed,the first method is log-normal shadowing model(LNSM),the model reflects the relationship between RSSI and distance,and the received RSSI is applied to the model to obtain the distance between nodes.The second method is RBF neural network algorithm.In this paper,the processed RSSI value and distance are taken as the input and output vectors of the network respectively.When the input and output vectors of the network are determined,the distance between nodes can be obtained by RSSI.In the two experimental environments,using the collected RSSI to get the distance,and compare the accuracy of the estimated distance.The simulation results verify the effectiveness of the RBF neural network algorithm.Then the node in the case of the drawing coordinate system is localization.In order to solve the problem of low accuracy of RSSI ranging,a distance optimization method is proposed to obtain a higher accurate ranging distance.Then trilateral positioning algorithm and weighted centroid algorithm are used to obtain the position.The simulation results show that the distance optimization algorithm can improve the ranging and localization accuracy,and the trilateral localization algorithm is better than the weighted algorithm used in this paper.