NDSL: Node Density-Based Subregional Localization in Large Scale Anisotropy Wireless Sensor Networks. (23rd November 2015)

• Record Type: Journal Article
• Authors: Tang, Zhanyong; Zhang, Jie; Wang, Liang; Han, Jinzhi; Fang, Dingyi; Wang, Anwen
• Other Names: He Jianping Academic Editor.
• Is Part Of: International journal of distributed sensor networks. (2015)
• Publication Date: 2015-11-23
• Subjects: Sensor networks -- Periodicals; Intelligent agents (Computer software) -- Periodicals; Multisensor data fusion -- Periodicals; 681.2
• DOI: 10.1155/2015/821352
• Languages: English
• ISSNs: 1550-1329

Abstract

Abstract : Localization is emerging as a fundamental component in wireless sensor network and is widely used in the fields of environmental monitoring, national defense and military, transportation, and so on. Current positioning system, however, can only locate an object's position in isotropy wireless sensor network with high accuracy but cannot locate it accurately in anisotropy wireless sensor network. Besides, past proposals only mentioned anisotropy to show that connectivity of network is different in each direction. However, how to quantify the degree of anisotropy is not clearly pointed out. This paper introduces NDSL (node density-based subregional localization), a positioning system that is used in anisotropy wireless sensor network. The network is divided into many subregions where the nodes density is relatively uniform and then corrects the single-hop distance for each beacon node to locate unknown nodes. We also use nodes distribution and signals distribution to build a model to evaluate the degree of anisotropy for anisotropy network. Through the analysis of the degree of anisotropy for different topologies, the results show that the model is consistent with the facts. Results from actual deployments and simulation experiments show that the accuracy of NDSL algorithm obviously improves compared with DV-Hop algorithm.