Research On Mobile Node Location Technology Of ZigBee Network In Indoor Environment

With the improvement of people's living standards and the development of positioning technology,the indoor node positioning has become a development demand of the contemporary society.Indoor node positioning is not only used in fire rescue,but also plays an important role in mining,industry,security and other aspects.Because ZigBee technology has the characteristics of short distance,low power consumption,self-organization,and high expansion,it is one of the most commonly used technologies for indoor positioning.But indoor node positioning algorithms often cause positioning errors due to ignoring the mobility of nodes.Therefore,how to accurately locate mobile nodes in indoor environments is a hot spot of universities and technology units.Based on the research of the algorithm,the research results are as follows:(1)Improved mobile node indoor positioning research based on particle swarm optimizationAn indoor mobile node location algorithm based on improved particle swarm optimization is proposed.The distance from the unknown node to the anchor node is calculated by the RSS ranging location model.Parameters in the ranging location model is determined by the Newton iteration algorithm and likelihood estimation.The bad effects of signal fading on positioning are determined.In order to prevent the particle swarm from falling into a local optimum,the inertia weight and accelerating constant of the particle swarm are optimized,and the crossover and mutation features of the genetic algorithm are used to improve the particle activity.The improved particle swarm optimization fully takes into account the mobility of the nodes,thereby reducing positioning errors.(2)Improved Monte Carlo mobile location algorithm based on least squares fittingAn improved Monte Carlo localization algorithm is proposed.The Monte Carlo Black Box(MBC)algorithm is used to optimize the sampling space,and the least squares are used to fit the nodal curve motion trajectory to predict the position of the nodal points.The optimal sampling area is further obtained.The node position calculation section introduces the weight distribution of nodes.The improved MCL positioning algorithm greatly reduces the sampling area and sampling time of the algorithm and reduces the positioning error.(3)ZigBee sensor network mobile node positioning monitoring platformThe positioning monitoring system consists of two parts: hardware platform and software design.After the user registers and logs in,it enters the indoor monitoring platform.This platform makes the experiment space location visible.In addition,use the Socket communication protocol to communicate with the mobile terminal to implement command positioning.