Abstract

Multi-hop localization schemes are low cost, easy to implement and suitable for large-scale application. However, in practical applications, the performances of multi-hop localizations are often affected by anisotropic networks, such as irregular deployment of nodes and uneven distribution of nodes. The anisotropic problem makes the application of multi-hop localization limited, so we propose a new multi-hop approach for anisotropic network. This scheme constructs the mapping relation between hop-counts and physical distance and this algorithm views the process of location estimation as a regression prediction. In addition, we employ the geometric precision factor (Geometric Dilution Precision) to select the anchors, and effectively avoid the problem of the error amplification of the geometric relations of the anchors. Theoretical analysis and simulation results show that our proposed method can adapt to different environments, and the method has the advantages of small overhead, high precision without setting complex parameters.

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