International Conference on Smart Homes and Health Telematics

ICOST 2011: Toward Useful Services for Elderly and People with Disabilities pp 80-87

A Subarea Mapping Approach for Indoor Localization

  • Shumei Zhang
  • Paul McCullagh
  • Chris Nugent
  • Huiru Zheng
  • Norman Black
Conference paper

DOI: 10.1007/978-3-642-21535-3_11

Volume 6719 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Zhang S., McCullagh P., Nugent C., Zheng H., Black N. (2011) A Subarea Mapping Approach for Indoor Localization. In: Abdulrazak B., Giroux S., Bouchard B., Pigot H., Mokhtari M. (eds) Toward Useful Services for Elderly and People with Disabilities. ICOST 2011. Lecture Notes in Computer Science, vol 6719. Springer, Berlin, Heidelberg

Abstract

Location information can be useful to construct a profile of a person’s activities of daily living. This paper proposes an approach with the aim of improving the accuracy and robustness of a location recognition approach based on RFID technology. A method was introduced for the optimal deployment of an RFID reader network, which aims to minimize the hardware cost whilst achieving high localization accuracy. A functional subarea mapping approach was proposed based on both a coarse-grained and a fine-grained method. Experimental results indicated that the coarse-grained mapping provided a higher overall accuracy of location detection. The average subarea location accuracy achieved based on coarse-grained and fine-grained data was 85.4% and 68.7%, respectively. Nevertheless it was found that the fine-grained mapping approach was capable of providing more information in relation to the details of the functional subareas. This implies that we need to balance the requirements between the size of the subareas and the location accuracy.

Keywords

subarea mapping localization RFID optimal deployment smart homes 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Shumei Zhang
    • 1
  • Paul McCullagh
    • 1
  • Chris Nugent
    • 1
  • Huiru Zheng
    • 1
  • Norman Black
    • 1
  1. 1.School of Computing and MathematicsUniversity of UlsterU.K.