Wireless Sensor Network(WSN)is composed of a large number of sensor nodes,with the ability to obtain data,process information and wireless communications,which are used to collect,process and transmit the information of the monitoring object.Node positioning is the premise of network monitoring and processing,but also one of the main support technologies.This article focuses on the wireless sensor network node positioning technology,the main work includes the following aspects:First,briefly described the basic structure and characteristics of the network,applications and key technologies and node positioning technology of wireless sensor network.On this basis,several classic algorithms based on ranging and non-ranging are introduced in detail.Then,based on the classical Distance Vector-Hop(DV-Hop)localization algorithm,an improved DV-Hop localization algorithm is proposed in view of the error caused by this algorithm in the estimation of distance.The improved algorithm uses the minimum mean square error criterion to calculate the average hop distance of the nodes.According to the uneven and irregular characteristics of the wireless sensor network,the estimated hop distance is weighted by the number of hops between the nodes.Near the anchor node,the weight factor is bigger,the maximum likelihood estimation method is used to get the physical coordinates of the unknown node by the weighted estimated distance.Through simulation,the validity and reliability of the algorithm are verified.Last,the intelligent optimization algorithm combine with DV-Hop localization is put forward to solve the problem that DV-Hop algorithm will produce large errors when calculating the physical coordinates of unknown nodes,namely DV-Hop localization algorithm based on Quantum Particle Swarm Optimization(QPSO).The node localization problem is transformed into a mathematical optimization problem.By constructing an appropriate objective function and optimizing iteratively,the coordinates of the unknown nodes can be optimized and corrected.The simulation results show that compared with the maximum likelihood estimation method and the classical particle swarm optimization algorithm,the optimized algorithm can significantly improve the positioning accuracy of the node without adding extra hardware cost. |