Manifold learning algorithms for localization in wireless sensor networks

N. Patwari, A.O. Hero
2004 IEEE International Conference on Acoustics, Speech, and Signal Processing  
If a dense network of static wireless sensors is deployed to measure an time-varying isotropic random field, then sensor data itself, rather than range measurements using specialized hardware, can be used to estimate a map of sensor locations. Furthermore, distributed and scalable sensor localization algorithms can be derived. We apply the manifold learning algorithms Isomap, Locally Linear Embedding (LLE), and Hessian LLE (HLLE). The HLLEbased estimator demonstrates the best bias and variance
more » ... bias and variance performance, but may not be robust for all random sensor deployments.
doi:10.1109/icassp.2004.1326680 dblp:conf/icassp/PatwariH04 fatcat:ijtmqizbc5cehl6khpkesgenpu