Wireless sensor network (WSN) is a self-organized network composed of a large number ofnodes. And the node localization is one of the technologies in WSN. The positioning algorithmcan be divided into the localization algorithm of rang-based and rang-free. The ifrst one is betterin the positioning accuracy, but it requires additional hardware and higher demands on theenvironment. While rang-free localization algorithm does not affected by hardware, environment,energy and other factors, positioned to nodes is only used the network connectivity informationbetween nodes. At present, most research of the localization algorithm is in the two-dimensionalenvironment. But in practical application, often it needs to provide three-dimensional informationof nodes. In this paper, the position accuracy is improved of two-dimensional algorithm, andextended the two-dimensional algorithm to the three-dimensional network. The main work of thispaper includes the following aspects.Aiming at the problem that the original DV-Hop algorithm for wireless sensor network and theexisting improvement of DV-Hop algorithm are easy to fall into local optimal solution, proposingan improvement algorithm based on Quantum Genetic Algorithm (QGA). Using the estimateddistance between the unknown nodes and the position of anchor nodes for the Quantum GeneticAlgorithm (QGA) to correct the position estimated by DV-Hop. Individuals are updated by usingbinary-quantum-coding, quantum rotate gate and mutation algorithm. During the updatingprocess, individual which are not change will be variation. It can disturb the population divorcefrom the local optimal solution.This algorithm in the positioning error and algorithm complexityis computational performance analysis and simulation in terms of positioning accuracy. Thesimulation results show that the improved algorithm is stability and can get the global optimalsolution. This algorithm can decrease the location error obviously.Wireless sensor network localization often belongs to3D positioning in the actual environment.The two-dimensional RSSI localization algorithm positioned is through the signal transmissionmodel. It can be well applied to the three-dimensional space. The two-dimensional RSSIlocalization algorithm is extended to3D space. The inlfuencing factors of three-dimensionalaccuracy in the RSSI localization algorithm are obtained by CRB analysis. For improving theaccuracy of the3D node localization algorithm of wireless sensor network, a new localizationalgorithm based on chaos particle swarm optimization algorithm was proposed by introducing thechaos optimization theory into the particle swarm optimization algorithm. Firstly, a new optimalindividual was reproduced by chaotic optimizing among the population of the best individuals,and then stochastic selected an individual from the population was replaced by the new optimalindividual, and searched the best coordinates through iterative method. Through analysis and simulation the error factors, that this algorithm can convergence fast and have good localizationaccuracy, and improved the problem of Particle Swarm Optimization that easily falling into localoptimal solution, also better at anti-noise performance.The research of localization algorithm is from two-dimensional localization extended to3D.The two-dimensional nodes localization is based on range-free localization algorithm, using thehop count for location calculation, saving the cost and energy consumption. This is suitable fortwo-dimensional localization environment that less demanding on hardware, better connectivitybetween nodes，smaller number of hops between the unknown nodes and anchor nodes.3D nodeslocalization is based on range-based localization algorithm. Using the intensity of the signal toestimate the distance between nodes and then positioning. This algorithm is applied in the highaccuracy requirement of the localization. The two algorithms are improving the positioningaccuracy, but increased the algorithm complexity and energy consumption. |