Manifold-based canonical correlation analysis for wireless sensor network localization
Signal-strength-based location estimation in wireless sensor networks is to locate the physical positions of unknown sensors via the received signal strengths. In this field, there are few localization researches sufficiently exploiting topology structures of the network in both signal space and physical space. The goal of this paper is to first establish two effective localization models based on specific manifold (or local) structures of both signal space and physical (location) space by using our previous locality preserving canonical correlation analysis (LPCCA) model and a newly-proposed locality correlation analysis (LCA) model, and then develop their corresponding novel location algorithms, called location estimationLPCCA (LELPCCA) and location estimationLCA (LELCA). Since both LPCCA and LCA relatively sufficiently take into account locality characteristics of the manifold structures in both the spaces, our localization algorithms developed from them consequently achieve better localization accuracy than other publicly available advanced algorithms. Copyright (C) 2011 John Wiley & Sons, Ltd.
wireless sensor network locality preserving canonical correlation analysis (lpcca) locality correlation analysis (lca) sensor localization manifold learning