A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks

摘要: We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed is based on self-organizing isometric embedding algorithm adaptively emphasizes most accurate range of measurements and naturally accounts for communication constraints within Each node chooses neighborhood sensors updates its estimate position by minimizing local cost function then passes this update neighboring sensors. Simulations demonstrate more robust measurement error than previous methods it can achieve comparable results using much fewer anchor methods.