Research On Localization Of Wireless Sensor Network Monitoring For Mine Based On Rigid Gragh Theory And Chicken Swarm Optimization

In mines with high temperature and humidity,dense person and equipment,and long line of power supplement,the distributed and wired monitoring system with the tree structure commonly used in existing mine networks has many defects and cannot meet the requirements of modern mine safety production management,such as the inability to expand system capacity and transmission efficiency;all monitoring equipment below branch will lose communication capacity when the branch fails;the fixed network structure cannot adapt to the dynamic changes of the mine working surface,etc.Because of the flexible and variable self-organizing network mode,Mine Wireless Sensor Network(WSN)can meet various requirements in the mine monitoring environment and provide real-time monitoring and early warning of the mine environment,production equipment operation status,production personnel location and vital signs.A large number of sensory monitoring nodes will be deployed in the environment,and the localization of the nodes is one of the key technologies of wireless sensor network.This paper takes the mine as the research object,adopts the method of theoretical research and numerical simulation,applies the wireless sensor network(WSN)to mine monitoring by combining with the requirements of the mine environment monitoring and the characteristics of the wireless sensor network,and a distribution algorithm is proposed.The node localization algorithm is used to improve the monitoring node positioning accuracy and positioning robustness in the underground roadway environment.The main research includes the following three aspects:(1)Select a distributed localization algorithm to accurately locate the nodes.The mine environment is complex,and there are great differences compared with the ground environment.It has the characteristics of long and narrow roadway,multiple branch roads,low node density requirements,fast node signal attenuation,and short communication radius,which will lead to wireless sensors in the mine environment.Node connectivity is reduced,and it has a greater adverse effect on the accuracy of downhole node positioning.For the characteristics of mine environment,distributed localization algorithm must be adopted.At the same time,in order to ensure the positioning accuracy,it is required that the anchor nodes should be laid evenly during the layout of the roadway nodes;(2)A node clustering algorithm based on rigid graph theory is proposed.Using the rigid graph theory,the uniqueness of downhole node positioning is transformed into the global rigidity of the frame composed of nodes and their connectivity.Although the whole underground mine node will satisfy the global rigidity when deployed,but after the clustering,because the nodes between the clusters can not be directly connected,it will destroy part of the connectivity,so whether the node frame within the cluster is globally rigid is still unknown.The relative position of the cluster nodes is not unique,the relative positioning within the cluster cannot be guaranteed unique,and the positioning accuracy of the entire network will be affected,and the localization algorithm will fail.In this paper,the initial anchor cluster is composed of the anchor node and its interconnected neighbors and extended to the surroundings.The remaining nodes determine the connectivity of the anchor cluster with the nodes in the rigid cluster.The other nodes are added to the rigid cluster in turn to synchronize multiple rigid clusters.Expand to the entire network;(3)A node localization algorithm based on flock optimization theory is proposed.The nodes in the same cluster are calculated according to the received signal strengths between the nodes in the cluster.The chicken group optimization algorithm is adopted,and the position set is the solution set.The relative distances between the node distances corresponding to each position set and the actual measured distance are calculated.The sum is the fitness function and relative positioning.After the nodes in the cluster are positioned relative to each other,their actual positions can be centered on the anchor nodes in the cluster and rotated at a certain angle.The actual position of different cluster nodes uses the flocks optimization algorithm,with different cluster rotation angles as the solution set.The sum of the relative errors of the node distances and the actual measured distances for each angle set corresponds to the fitness function and absolute positioning.Through simulation and verification,this localization algorithm has better performance in mine simulation environment,different roadway environment,node density,communication radius and anchor node ratio,and has good robustness.