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Indoor Localization with Adaptive Channel Model Estimation in WiFi Networks

  • Yung-Fa HuangEmail author
  • Yi-Hsiang Hsu
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 513)

Abstract

This paper investigates the indoor localization based on channel estimation with the received signal strength (RSS) of three access points (APs) in Wi-Fi networks. In this paper, the proposed adaptive channel estimation for the path loss exponent (PLE) is based on the root mean square error (RMSE) of RSS of the APs. The PLE effects are investigated and then an adaptive PLE estimation is proposed to improve the indoor localization. Experimental results show that the proposed adaptive modeling of PLEs can effectively decrease the localization error.

Keywords

Indoor localization Received signal strength Path loss exponent Root mean square error 

Notes

Acknowledgements

This work was funded in part by Ministry of Science and Technology of Taiwan under Grant MOST 106-2221-E-324-020.

References

  1. 1.
    Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) A survey on sensor networks. IEEE Commun Mag 40(8):102–114CrossRefGoogle Scholar
  2. 2.
    Royer EM, Toh CK (1999) A review of current routing protocols for Ad-hoc mobile wireless networks. IEEE Pers Commun 6(4):46–55CrossRefGoogle Scholar
  3. 3.
    Boman J, Taylor J, Ngu AH (2014) Flexible IoT middleware for integration of things and applications. In: IEEE international conference on collaborative computing: networking, applications and worksharing, Miami, Florida, pp 481–488Google Scholar
  4. 4.
    Xiong SM, Wang LM, Qu XQ, Zhan YZ (2009) Application research of WSN in precise agriculture irrigation. In: International conference on environmental science and information application technology, Wuhan, pp 297–300Google Scholar
  5. 5.
    Gui L, Val T, Wei A, Taktak S (2014) An adaptive range-free localization protocol in wireless sensor networks. Int J Ad Hoc Ubiquitous Comput 15(1/2/3):1–20CrossRefGoogle Scholar
  6. 6.
    Lin A, Zhang J, Jiang X, Zhang J (2016) A reliable and energy-efficient outdoor localization method for smartphones. Int J Ad Hoc Ubiquitous Comput 23(3/4):230–242CrossRefGoogle Scholar
  7. 7.
    Kharidia SA, Ye Q, Sampalli S, Cheng J, Du H, Wang L (2014) HILL: ahybrid indoor localization scheme. In: International conference on mobile Ad-hoc and sensor network, Maui, Hawaii, pp 201–206Google Scholar
  8. 8.
    Huang H, Zhou J, Li W, Zhang J, Zhang X, Hou G (2016) Wearable indoor localisation approach in Internet of Things. IET Netw 5(5):122–126CrossRefGoogle Scholar
  9. 9.
    Dag T, Arsan T (2018) Received signal strength based least squares lateration algorithm for indoor localization. Comput Electr Eng 66:114–126CrossRefGoogle Scholar
  10. 10.
    Ye X, Yin X, Cai X, Pérez Yuste A, Xu H (2017) Neural-network-assisted UE localization using radio-channel fingerprints in LTE networks. IEEE Access 5(5):12071–12087CrossRefGoogle Scholar
  11. 11.
    Huang Y-F, Jheng Y-T, Chen H-C (2010) Performance of an MMSE based indoor localization with wireless sensor networks. In: The sixth international conference on networked computing and advanced information management (NCM 2010), Seoul, Korea, pp 671–675Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information and Communication EngineeringChaoyang University of TechnologyTaichungTaiwan

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