Research On Improved Algorithms For Mobile Nodes Location In Wireless Sensor Networks Based On Monte Carlo

With the development of wireless sensor technology,Wireless Sensor Networks(WSN)is applied to all areas of people's work and life.The wireless sensor network senses,collects and processes related information in the network coverage area through cooperation among nodes,and realizes information interaction with the observer.Positioning is one of the foundations and key technologies for wireless sensor network applications.Due to limitations in energy consumption,cost,and scalability,current GPS and other positioning technologies are clearly not suitable for large-scale wireless sensor networks.Therefore,researching and designing a high-efficiency and low-power WSN node self-localization algorithm has important research value.The Monte Carlo Localization(MCL)algorithm is the first non-ranging positioning method for mobile node positioning.The algorithm uses the mobile characteristics of the node to optimize the positioning performance,which provides a new idea for positioning research.By analyzing the advantages and disadvantages of the MCL algorithm in wireless sensor networks,two improved algorithms are proposed based on them in his paper.The positioning performance is improved by enhancing the filtering conditions,improving the filtering mechanism,and optimizing the weights.The main research work done in this paper is as follows:(1)The background and development history of wireless sensor networks are introduced.The working principle,structural characteristics and specific applications of wireless sensor networks,as well as the research status of several important research directions are introduced in detail.The related knowledge of node location is introduced.Several typical positioning algorithms,in which the Monte Carlo positioning algorithm is described and analyzed.(2)Aiming at the insufficiency of the positioning accuracy of the classical Monte Carlo algorithm in dynamic wireless sensor networks,an improved algorithm based on distance estimation-DEMCL algorithm is proposed to introduce a distance without direct ranging in the filtering stage of Monte Carlo positioning algorithm.The estimation method optimizes the sample set by a stricter filtering step to reduce the positioning error.The simulation results show that the positioning accuracy and network coverage of the algorithm are improved.(3)Aiming at the problem of weak filtering mechanism and insufficient sample diversity of Monte Carlo algorithm,MSMCL algorithm is proposed,which can fully exploit the third class available.The filtering process of the anchor node information enhancement algorithm introduces the MeanShift vector of the sample to optimize its weight,which protects the sample diversity.The experimental results show that the positioning accuracy and network coverage of the algorithm are effectively improved without adding additional communication overhead.