An adaptive distributed localisation in wireless sensor network

Many applications of wireless sensor networks (WSN) in industry can benefit from fine-grained localisation. In this paper, we propose an accurate, distributed localisation method which uses connectivity measurements to localise sensor nodes in WSN. The proposed method is based on a manifold learning embedding algorithm that adaptively emphasises the most accurate range measurements and naturally accounts for communication constraints within the WSN. Each node adaptively chooses a neighbourhood of sensor, updates its position estimate by minimising a local cost function and then passes this update to neighbouring sensors. Simulation results demonstrate that the proposed method is more robust to measurement errors than previous proposals and it can achieve comparable results using many fewer anchor nodes than previous methods.