文章基本信息

标题：Decomposition Based Localization for Anisotropic Sensor Networks
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作者：Baojian Gao ; Xiaoning Zhao ; Jun Wang 等
期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/805061
出版社：Hindawi Publishing Corporation
摘要：Range-free localization algorithms have caused widespread attention due to their low cost and low power consumption. However, such schemes heavily depend on the assumption that the hop count distance between two nodes correlates well with their Euclidean distance, which will be satisfied only in isotropic networks. When the network is anisotropic, holes or obstacles will lead to the estimated distance between nodes deviating from their Euclidean distance, causing a serious decline in localization accuracy. This paper develops HCD-DV-Hop for node localization in anisotropic sensor networks. HCD-DV-Hop consists of two steps. Firstly, an anisotropic network is decomposed into several different isotropic subnetworks, by using the proposed Hop Count Based Decomposition (HCD) scheme. Secondly, DV-Hop algorithm is carried out in each subnetwork for node localization. HCD first uses concave/convex node recognition algorithm and cleansing criterion to obtain the optimal concave and convex nodes based on boundary recognition, followed by segmentation of the network’s boundary. Finally, the neighboring boundary nodes of the optimal concave nodes flood the network with decomposition messages; thus, an anisotropic network is decomposed. Extensive simulations demonstrated that, compared with range-free DV-Hop algorithm, HCD-DV-Hop can effectively reduce localization error in anisotropic networks without increasing the complexity of the algorithm.