IJMLC 2014 Vol. 4(6): 505-509 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2014.V6.463

Wi-Fi Based Indoor Next Location Prediction Using Mixed State-Weighted Markov-Chain Model

Jian Huang, Daniel Dahlmeier, Ziheng Lin, Boon-Khai Ang, Mun-Lie Seeto, and Hendy Shi

Abstract—Due to wide adoption of smart phones, Location Based Services (LBS) that leverage the user’s location information have recently attracted an increasing amount of interest. As one of the most promising LBS technology, indoor Wi-Fi based positioning can provide relatively precise location information of Wi-Fi enabled mobile users at a low cost. In this paper, we propose a model called “Mixed State-Weighted Markov-chain Model” (MSWMM) to predict the next location of a user given his previous n locations, where the locations are derived from Wi-Fi based positioning. MSWMM is an improved version of the “Mixed Markov-chain Model” (MMM) and it takes into account the visited frequency of the same location and not just the transition probability between adjacent locations. In the experiment of comparing with MMM for n=2, MSWMM yields a significant 20.38% improvement of prediction accuracy over MMM.

Index Terms—Markov-chain model, next location prediction, Wi-Fi based positioning.

Jian Huang, Boon-Khai Ang, Mun-Lie Seeto, and Hendy Shi are with the National University of Singapore, 25 Heng Mui Keng Terrace, 119615 Singapore (e-mail: A0092599@nus.edu.sg, A0092597@nus.edu.sg, A0092671@nus.edu.sg, A0092709@nus.edu.sg).
Daniel Dahlmeier is with SAP Asia Pte Ltd, CREATE Tower 1 Create Way, 138602 Singapore (e-mail: d.dahlmeier@sap.com).
Ziheng Lin is with Singapore Press Holdings, 1000 Toa Payoh North, 318994 Singapore (e-mail: linziheng@gmail.com).

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Cite: Jian Huang, Daniel Dahlmeier, Ziheng Lin, Boon-Khai Ang, Mun-Lie Seeto, and Hendy Shi, "Wi-Fi Based Indoor Next Location Prediction Using Mixed State-Weighted Markov-Chain Model," International Journal of Machine Learning and Computing vol. 4, no. 6, pp. 505-509, 2014.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), EI (INSPEC, IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net