A two-tier positioning algorithm for wireless networks with diverse measurement types

A variety of measurement methods have been developed to obtain positions of nodes in wireless networks. However, most of existing positioning methods can only use limited types of measurement. These algorithms fail to exploit arbitrary measurement methods to improve the accuracy of positioning. In this paper, we propose a high-accuracy positioning algorithm which can utilize unlimited types of measurement. Our algorithm first selects a set of nodes equipped with more accurate measurement mechanisms to locate nodes whose positions are unknown. By this selection, a system of heterogenous equations is built. Then, our algorithm uses the classical genetic method to solve equations which commonly are hard to solve. The comprehensive simulation shows that our algorithm significantly improves the accuracy of positioning compared with existing methods.