Study On The Localization Algorithms To Large-scale Random Wireless Sensor Networks In Disturbance Environment

Node self-localization and target localization is a basic function of WSN, playing a key role in the effectiveness of WSN. Aiming at large-scale random sensor networks, an algorithm of node self-localization in large-scale random deployed environment has been proposed in this thesis, an improved DV-hop algorithm based on the residual weighted in large-scale anisotropy network has been put forward, and a selection scheme of the reference nodes in disturbance environment has been presented to improve the localization accuracy of the target in the network, resulting in a low computational complexity.A node self-localization algorithm called DV-NNA (DV-hop with the Number of Common Neighbor Based on APIT) which is suitable for large-scale random network has been proposed. The average one-hop distances of the anchor nodes was refined by means of least squares method, the estimated distances of the unknown nodes covered by the anchor nodes communication range was refined by the information of Neighbors node, a reasonable selection of anchor nodes based on APIT algorithm was proposed. The simulation results show that DV-NNA algorithm achieves a good effect on distance estimation and the coordinate calculation of the unknown nodes, having a high accuracy and efficiency.For the characteristics of the heterogeneous network, an improved DV-hop algorithm has been proposed based on the residual weighted in this thesis. Using the residual weighted algorithm, the average hop distance has been optimized and the effect on the average hop distance estimation due to the anchor nodes having circuity phenomenon has been weaken as well. Besides, the proposed algorithm has fused the least hops method of Amorphous in estimation. The simulation experiments on DV-hop, Amorphous algorithm and the proposed algorithm have been carried out in three anisotropy networks. Compared with other algorithms in the root mean square error and variance, the proposed algorithm has a higher location accuracy and stable performance.Aiming at the disturbance environmentan, an elaborate analysis on the interference factors and an effective measure on restraining the disturbance errors have been performed in this thesis. According to the weakening and restraining method, a selection scheme of the reference nodes under the interference situation has been presented. The experiment results show that compared with other schemes, the proposed selection scheme has lower computational complexity in target locating and better accuracy with small location variance using different environmental parameters. At the same time, this selection scheme has effectively restrained the interference effect on the estimation of target position.