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We then propose a semi-supervised learning algorithm based on manifold to obtain the absolute locations of the devices. Both solutions are finally combined together in terms of graph Laplacian. Extensive experiments are conducted in wireless local-area networks, wireless sensor networks and radio frequency identification networks. The experimental results show that we can achieve high accuracy with much less calibration effort as compared to several previous systems.","doi":null,"url":"https://ijcai.org/proceedings/2007/349","pdf":"https://ijcai.org/proceedings/2007/0349.pdf","markdown":"https://ijcai.org/proceedings/2007/349.md","bibtex":"https://ijcai.org/proceedings/2007/bibtex/349","volume":{"slug":"2007","title":"Proceedings of the Twentieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2007","url":"https://ijcai.org/proceedings/2007/","year":2007,"location":"Hyderabad, India","dates":"","editors":"Ramon López de Mántaras","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":349,"pages":{"first":2166,"last":2170},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Bahl and Padmanabhan, 2000] P. Bahl and V.N. Padmanab- han. 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