A New Multi-Hop Localization Based on l_2 Constraint Least Square for Anisotropic Networks

Abstract

Multi-hop localization is a common method for wireless networks. However, when this network is anisotropic, the performance of multi-hop localization is greatly reduced due to deviation of the relationship between hop counts and physical distance. In order to improve the performance of multi-hop localization in anisotropic networks, this paper uses the l_2 constraint least square to build a mapping relationship that represents the anisotropy of a network based on anchors. During transformations between the hops and the physical distances, the proposed algorithm can prevent over-fitting through the constraint of some space. The proposed algorithm has strong adaptability to the complex deployment environment; it overcomes the shortcoming of the traditional algorithm which applies only to the isotropic network. We also compare our method with several related methods, and the results show that our proposed method is more efficient than others in different topological networks. Furthermore, high accuracy can be obtained by this method without setting complex parameters.