Skip to main content
Log in

Neural network based mobile phone localization using Bluetooth connectivity

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Location information is useful for mobile phones. There exists a dilemma between the relatively high price of GPS devices and the dependence of location information acquisition on GPS for most phones in current stage. To tackle this problem, in this paper, we investigate the position inference of phones without GPS according to Bluetooth connectivity and positions of beacon phones. With the position of GPS-equipped phones as beacons and with the Bluetooth connections between neighbor phones as constraints, we formulate the problem as an optimization problem defined on the Bluetooth network. The solution to this optimization problem is not unique. Heuristic information is employed to improve the performance of the result in the feasible set. Recurrent neural networks are developed to solve the problem distributively in real time. The convergence of the neural network and the solution feasibility to the defined problem are both theoretically proven. The hardware implementation of the proposed neural network is also explored in this paper. Simulations and comparisons with different application backgrounds are considered. The results demonstrate the effectiveness of the proposed method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Sohn T, Li KA, Lee G, Smith IE, Scott J, Griswold WG (2005) Place-its: a study of location-based reminders on mobile phones. In: Beigl M, Intille SS, Rekimoto J, Tokuda H (eds) Ubicomp. Springer, Berlin, pp 232–250

  2. Hay S, Harle R (2009) Bluetooth tracking without discoverability. In: Choudhury T, Quigley A, Strang T, Suginuma K (eds) Location and context awareness, vol 5561 of lecture notes in computer science, Springer, Berlin/Heidelberg, pp 120–137

  3. Pei L, Chen R, Liu J, Tenhunen T, Kuusniemi H, Chen Y (2010) Inquiry-based bluetooth indoor positioning via rssi probability distributions. In: Proceedings of the 2010 second international conference on advances in satellite and space communications, SPACOMM ’10, Washington, DC, USA. IEEE Computer Society, pp 151–156

  4. Ergut S, Rao R (2008) Localization via multipath strengths in a cdma2000 cellular network using neural networks. In: 2008 IJCNN, pp 4066–4069

  5. Ergut S, Rao R, Dural O, Sahinoglu Z (2008) Localization via tdoa in a uwb sensor network using neural networks. In: 2008 IEEE international conference on communications, pp 2398–2403

  6. Li S, Chen S, Lou Y, Lu B, Liang Y (2012) A recurrent neural network for inter-localization of mobile phones. In: IJCNN 2012

  7. Skowronski MD, Harris JG (2007) Noise-robust automatic speech recognition using a predictive echo state network. IEEE Trans Audio Speech Lang Process 15(5):1724–1730

    Article  Google Scholar 

  8. Li S, Meng MQH, Chen W (2007) Sp-nn: a novel neural network approach for path planning. In: IEEE international conference on robotics and biomimetics, 2007, pp 1355 –1360

  9. Ogata T, Nishide T, Kozima H, Komatani K, Okuno HG (2010) Inter-modality mapping in robot with recurrent neural network. Pattern Recognit Lett 31(12):1560–1569

    Article  Google Scholar 

  10. Smith KA (1999) Neural networks for combinatorial optimization: a review of more than a decade of research. INFORMS J Comput 11:15–34

    Article  MathSciNet  MATH  Google Scholar 

  11. Wu N (1997) The maximum entropy method (Springer series in information sciences). Springer, Berlin

    Book  Google Scholar 

  12. Vicsek T, Czirók A, Ben-Jacob E, Cohen I, Shochet O (1995) Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett 75:1226–1229

    Article  Google Scholar 

  13. Saber RO, Murray RM (2003) Flocking with obstacle avoidance: cooperation with limited communication in mobile networks. In: Proceedings of 42nd IEEE conference on decision and control, Dec 2003, vol 2, pp 2022–2028

Download references

Acknowledgments

The authors would like to acknowledge the constant motivation by the following motto by Franklin D. Roosevelt “The only limit to our realization of tomorrow will be our doubts of today.”

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shuai Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, S., Liu, B., Chen, B. et al. Neural network based mobile phone localization using Bluetooth connectivity. Neural Comput & Applic 23, 667–675 (2013). https://doi.org/10.1007/s00521-012-0950-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-0950-1

Keywords

Navigation