research-article

Bluetooth aided mobile phone localization: A nonlinear neural circuit approach

Authors:

Bo LiuAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 13, Issue 4

Article No.: 78, Pages 1 - 15

https://doi.org/10.1145/2560018

Published: 10 March 2014 Publication History

Abstract

It is meaningful to design a strategy to roughly localize mobile phones without a GPS by exploiting existing conditions and devices especially in environments without GPS availability (e.g., tunnels, subway stations, etc.). The availability of Bluetooth devices for most phones and the existence of a number of GPS equipped phones in a crowd of phone users enable us to design a Bluetooth aided mobile phone localization strategy. With the position of GPS equipped phones as beacons, and with the Bluetooth connection between neighbor phones as proximity constraints, we formulate the problem into an inequality problem defined on the Bluetooth network. A recurrent neural network is 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 architecture of the proposed neural network is also given in this article. As applications, rough localizations of drivers in a tunnel and localization of customers in a supermarket are explored and simulated. Simulations demonstrate the effectiveness of the proposed method.

References

[1]

Mike Y. Chen, Timothy Sohn, Dmitri Chmelev, Dirk Haehnel, Jeffrey Hightower, Jeff Hughes, Anthony Lamarca, Fred Potter, Ian Smith, and Alex Varshavsky. 2006. Practical metropolitan-scale positioning for gsm phones. In Proceedings of the 8th International Conference on Ubiquitous Computing. Springer, 225--242.

Digital Library

[2]

Y. C. Cheng, Y. Chawathe, A. LaMarca, and J. Krumm. 2005. Accuracy characterization for metropolitan-scale wi-fi localization. In Proceedings of the 3rd International Conference on Mobile Systems, Applications, and Services (MobiSys'05). ACM, New York, 233--245.

Digital Library

[3]

L. Doherty, K. S. J. Pister, and E. L. Ghaoui. 2001. Convex position estimation in wireless sensor networks. In Proceedings of the 20th Annual Joint Conference of the IEEE Computer and Communications Societies (INFOCOM'01). Vol. 3, 1655--1663.

[4]

E. Doukhnitch, M. Salamah, and E. Ozen. 2008. An efficient approach for trilateration in 3D positioning. Comput. Commun. 31, 4124--4129.

Digital Library

[5]

S. Feldmann, K. Kyamakya, A. Zapater, and Z. Lue. 2003. An indoor bluetooth-based positioning system: Concept, implementation and experimental evaluation. In Proceedings of the International Conference on Wireless Networks. 109--113.

[6]

Simon Hay and Robert Harle. 2009. Bluetooth tracking without discoverability. In Location and Context Awareness, T. Choudhury, A. Quigley, T. Strang, and K. Suginuma, Eds., Lecture Notes in Computer Science, vol. 5561, Springer, 120--137.

Digital Library

[7]

T. He, C. Huang, Brian M. Blum, J. A. Stankovic, and T. Abdelzaher. 2003. Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th Annual International Conference on Mobile Computing and Networking (MobiCom'03). ACM, New York, 81--95.

Digital Library

[8]

M. A. Hsieh, A. Cowley, V. Kumar, and C. J. Taylor. 2008. Maintaining network connectivity and performance in robot teams. J. Field Rob. 25, 1--2, 111--131.

Digital Library

[9]

K. Kaemarungsi and P. Krishnamurthy. 2004. Properties of indoor received signal strength for wlan location fingerprinting. In Proceedings of the 1st Annual International Conference on Mobile and Ubiquitous Systems: Networking and Services (MOBIQUITOUS'04). 14--23.

[10]

Oliver Keiser, Philipp Sommer, and Bernhard Tellenbach. 2006. Bluelocation ii: A localization infrastructure for bluetooth enabled mobile devices. Tech Rep., Swiss Federal Institute of Technology.

[11]

H. Khalil. 2002. Nonlinear Systems. Prentice Hall.

[12]

A. R. Kulaib, R. M. Shubair, M. A. Al-Qutayri, and J. W. P. Ng. 2011. An overview of localization techniques for wireless sensor networks. In Proceedings of the International Conference on Innovations in Information Technology. 167--172.

[13]

S. Li, S. Chen, B. Liu, Y. Li, and Y. Liang. 2012. Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks. Neurocomputing.

Digital Library

[14]

S. Li, S. Chen, Y. Lou, B. Lu, and Y. Liang. A recurrent neural network for inter-localization of mobile phones. In Proceedings of the IEEE International Joint Conference on Neural Networks.

[15]

S. Li, M. Q. H. Meng, and W. Chen. 2007. Sp-nn: A novel neural network approach for path planning. In Proceedings of the IEEE International Conference on Robotics and Biomimetics. 1355--1360.

[16]

Ling Pei, Ruizhi Chen, Jingbin Liu, Tomi Tenhunen, Heidi Kuusniemi, and Yuwei Chen. 2010. Inquiry-based bluetooth indoor positioning via RSSI probability distributions. In Proceedings of the 2nd International Conference on Advances in Satellite and Space Communications (SPACOMM'10). IEEE, 151--156.

Digital Library

[17]

Aswin N. Raghavan, Harini Ananthapadmanaban, Manimaran Sivasamy Sivamurugan, and Balaraman Ravindran. 2010. Accurate mobile robot localization in indoor environments using bluetooth. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA'10). 4391--4396.

[18]

J. Reich, V. Misra, D. Rubenstein, and G. Zussman. 2011. Connectivity maintenance in mobile wireless networks via constrained mobility. In Proceedings of the Annual Joint Conference of the IEEE Computer and Communications Societies. 927--935.

[19]

M. D. Skowronski and J. G. Harris. 2007. Noise-robust automatic speech recognition using a predictive echo state network. IEEE Trans. Audio Speech Lang. Process. 15, 5, 1724--1730.

Digital Library

[20]

K. A. Smith. 1999. Neural networks for combinatorial optimization: a review of more than a decade of research. INFORMS J. Comput. 11, 15--34.

Digital Library

[21]

T. Sohn, K. A. Li, G. Lee, I. E. Smith, J. Scott, and W. G. Griswold. 2005. Place-its: A study of location-based reminders on mobile phones. In Proceedings of the ACM International Conference on Ubiquitous Computing. M. Beigl, S. Intille, J. Rekimoto, and H. Tokuda, Eds., Lecture Notes in Computer Science, vol. 3660, Springer, 232--250.

Digital Library

[22]

D. Tse and P. Viswanath. 2005. Fundamentals of Wireless Communication. Cambridge University Press, Cambridge, UK.

Digital Library

[23]

A. Varshavsky, E. Lara, J. Hightower, A. LaMarca, and V. Otsason. 2007. GSM indoor localization. Pervasive Mob. Comput. 3, 698--720.

Digital Library

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Liao BHua CXu QCao XLi S(2024)Inter-robot management via neighboring robot sensing and measurement using a zeroing neural dynamics approachExpert Systems with Applications10.1016/j.eswa.2023.122938244(122938)Online publication date: Jun-2024
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Index Terms

Bluetooth aided mobile phone localization: A nonlinear neural circuit approach
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks

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Published In

cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 13, Issue 4

Regular Papers

November 2014

647 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/2592905

Editor:
Sandeep K. Shukla
Virginia Tech, USA

Issue’s Table of Contents

Copyright © 2014 ACM.

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 10 March 2014

Accepted: 01 May 2012

Revised: 01 February 2012

Received: 01 November 2011

Published in TECS Volume 13, Issue 4

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https://doi.org/10.1109/TNNLS.2022.3175595
Liao BHua CXu QCao XLi S(2024)Inter-robot management via neighboring robot sensing and measurement using a zeroing neural dynamics approachExpert Systems with Applications10.1016/j.eswa.2023.122938244(122938)Online publication date: Jun-2024
https://doi.org/10.1016/j.eswa.2023.122938
Poongadan SLineesh M(2024)Non-linear Time Series Prediction using Improved CEEMDAN, SVD and LSTMNeural Processing Letters10.1007/s11063-024-11622-z56:3Online publication date: 6-May-2024
https://doi.org/10.1007/s11063-024-11622-z
Sun ZXu CWang GLiu YZhao LDong M(2024)Noise-tolerant zeroing neural network control for a novel compliant actuator in lower-limb exoskeletonsNeural Computing and Applications10.1007/s00521-024-09789-636:22(13647-13663)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00521-024-09789-6
Hua CCao XXu QLiao BLi S(2023)Dynamic Neural Network Models for Time-Varying Problem Solving: A Survey on Model StructuresIEEE Access10.1109/ACCESS.2023.329004611(65991-66008)Online publication date: 2023
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Wang TZhang FXin JLiu Y(2022)Grouping-Based Optimization Method for Multirobot System Pattern FormationIEEE Systems Journal10.1109/JSYST.2021.312254816:3(3679-3688)Online publication date: Sep-2022
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Ayyadurai MSeetha JHaque SJuliana RKarthikeyan C(2022)Routing Algorithm for Underwater Acoustic Sensor NetworkNeural Processing Letters10.1007/s11063-022-10891-w55:1(441-457)Online publication date: 22-Jun-2022
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Yu YWang XZhong SYang NTashi N(2021)Extended Robust Exponential Stability of Fuzzy Switched Memristive Inertial Neural Networks With Time-Varying Delays on Mode-Dependent Destabilizing Impulsive Control ProtocolIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.297854232:1(308-321)Online publication date: Jan-2021
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