A hyperbolic distance measurement model for range-free localization in anisotropic networks

This paper presents a hyperbolic distance measurement model where the difference between the distances from a normal node to two anchors is defined as the sum of the difference between the distances from another anchor to the anchors and an error. A hyperbolic range-free localization algorithm is then introduced based on the proposed model for anisotropic networks. The proposed algorithm estimates the location of each normal node by selecting hyperbolic distance measurements with maximum errors less than a predefined threshold. Simulation results show the superiority of the proposed algorithm over existing range-free localization algorithms.