Constructing adaptive indoor radio maps for dynamic wireless environments

Xiaodong Cai, Ling Chen, Gencai Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

In received signal strength fingerprints based indoor localization systems, the radio map built by labeled wireless fingerprints is easily outdated over time, while re-calibrating the overall radio map is time consuming. To avoid the tedious task, we propose to employ manifold alignment to label the current radio map from outdated radio map, with the constraint of the Hidden Markov Model trained by trajectories of the received signal strength readings. Manifold alignment can align the low-dimensional manifold structures of two different data sets and transfer knowledge across them. Transition matrix generated by Hidden Markov Model is used to constrain the alignment of manifolds. The proposed algorithms are tested in a real world ZigBee environment. Experiment results show that our method outperforms state-of-the-art transfer learning algorithms.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013
Pages41-47
Number of pages7
DOIs
StatePublished - Dec 1 2013
Event10th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and 10th IEEE International Conference on Autonomic and Trusted Computing, ATC 2013 - Vietri sul Mare, Italy
Duration: Dec 18 2013Dec 21 2013

Publication series

NameProceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013

Other

Other10th IEEE International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and 10th IEEE International Conference on Autonomic and Trusted Computing, ATC 2013
CountryItaly
CityVietri sul Mare
Period12/18/1312/21/13

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Keywords

  • Hidden Markov Model
  • Indoor Localization
  • Manifold Alignment
  • Transfer Learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

Cite this

Cai, X., Chen, L., & Chen, G. (2013). Constructing adaptive indoor radio maps for dynamic wireless environments. In Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013 (pp. 41-47). [06726189] (Proceedings - IEEE 10th International Conference on Ubiquitous Intelligence and Computing, UIC 2013 and IEEE 10th International Conference on Autonomic and Trusted Computing, ATC 2013). https://doi.org/10.1109/UIC-ATC.2013.20