| Paper: | SS-5.3 | ||
| Session: | Signal Processing for Wireless Sensor Networks II | ||
| Time: | Wednesday, May 19, 13:34 - 13:51 | ||
| Presentation: | Special Session Lecture | ||
| Topic: | Special Sessions: Signal Processing for Wireless Sensor Networks | ||
| Title: | MANIFOLD LEARNING ALGORITHMS FOR LOCALIZATION IN WIRELESS SENSOR NETWORKS | ||
| Authors: | Neal Patwari; University of Michigan | ||
| Alfred O. Hero III; University of Michigan | |||
| Abstract: | If a dense network of static wireless sensors is deployed to measure an 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 HLLE-based estimator demonstrates the best bias and variance performance, but may not be robust for all random sensor deployments. | ||
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