Hostname: page-component-586b7cd67f-gb8f7 Total loading time: 0 Render date: 2024-11-28T16:24:57.920Z Has data issue: false hasContentIssue false
  • English
  • Français

Implementation Technology for Localising a Group of Mobile Nodes in a Mobile Wireless Sensor Network

Published online by Cambridge University Press:  30 July 2014

Seong Yun Cho
Seong Yun Cho*
Affiliation:
(Department of Applied Robotics, Kyungil University, South Korea)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper addresses a simple implementation technology for localising a group of mobile nodes using the Peer-To-Peer (P2P) ranging measurements obtained from a Mobile Wireless Sensor Network (MWSN). In recent years, MWSN-based multi-target localisation technologies have been investigated with alacrity. However, it is difficult to implement P2P ranging and data transformation for multi-target localisation iteratively using single channel MWSN. Also, there are critical problems in the conventional localisation algorithms associated with the estimation errors owing to the erroneous linearization of a nonlinear range equation and bad relative locations of the reference nodes and target node. In this paper, very simple P2P ranging and connection data sharing procedures that can be easily implemented in single channel MWSN are established. Moreover, an adaptive localisation algorithm is presented to reduce the estimation errors that may occur in the conventional localisation algorithms. To verify the performance of the presented implementation technology, a localisation system prototype is developed and the presented procedures and algorithm are embedded in the prototype. Through actual experiments, the validity of the localisation system containing the proposed procedures and algorithm is demonstrated.

Keywords

Mobile wireless sensor networksPeer-to-peer rangingConnection data sharingAdaptive localisation algorithm
Type
Research Article
Information
The Journal of Navigation , Volume 67 , Issue 6 , November 2014 , pp. 1089 - 1108
Copyright
Copyright © The Royal Institute of Navigation 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Ahn, H.S. and Ko, K.H. (2009). Simple pedestrian localization algorithms based on distributed wireless sensor networks. IEEE Trans. Industrial Electronics, 56, 4296–4302.Google Scholar
Biton, I., Koifman, M. and Bar-Itzhack, I.Y. (1998). Improved direct solution of the global positioning system equation. Journal of Guidance, Control, and Dynamics, 21, 45–49.Google Scholar
Chen, H., Wang, G., Wang, Z., So, H.C. and Poor, H.V. (2012). Non-line-of-sight node localization based on semi-definite programming in wireless sensor networks. IEEE Trans Wireless Communications, 11, 108–116.Google Scholar
Cho, H. and Kim, S.W. (2010). Mobile robot localization using biased chirp-spread-spectrum ranging. IEEE Trans. Industrial Electronics, 57, 2826–2835.Google Scholar
Cho, S.Y. (2010). Localization of the arbitrary deployed APs for indoor wireless location-based applications. IEEE Trans. on Consumer Electronics, 56, 532–539.Google Scholar
Cho, S.Y. and Choi, Y.W. (2010). Access point-less wireless location method based on peer-to-peer ranging of impulse radio ultra-wideband. IET-Radar, Sonar and Navigation, 4, 733–743.Google Scholar
Cho, S.Y. and Kim, B.D. (2013). Linear closed-form solution for wireless localisation with ultra-wideband/chirp spread spectrum signals based on difference of squared range measurements. IET Wireless Sensor Systems, 3, 255–265.Google Scholar
Hazas, M., Scott, J. and Krumm, J. (2004). Location-aware computing comes of age. IEEE Computer, 37, 95–97.Google Scholar
Hightower, J. and Borriello, G. (2001). Location systems for ubiquitous computing. IEEE Computer, 34, 57–66.Google Scholar
Hur, H. and Ahn, H.S. (2010). A circuit design for ranging measurement using chirp spread spectrum waveform. IEEE Sensors Journal, 10, 1774–1778.Google Scholar
Jhi, H.L., Chen, J.C., Lin, C.H. and Huang, C.T. (2012). A factor-graph-based TOA location estimator. IEEE Trans. Wireless Communications, 11, 1764–1773.Google Scholar
Kang, H., Seo, G.W. and Lee, J.H. (2009). Error compensation for CSS-based localization system. Proceedings of the World Congress on Engineering and Computer Science 2009, San Francisco, CA.Google Scholar
Kaplan, E.D. (1996). Understanding GPS principles and applications, Artech House, Inc.Google Scholar
Kitasuka, T., Nakanishi, T. and Fukuda, A. (2003). Design of WiPS: WLAN-based indoor positioning system. Korea Multimedia Society, 7, 15–29.Google Scholar
Kolodziej, K.W. and Hjelm, J. (2006). Local positioning systems: LBS Applications and Services, Taylor & Francis.Google Scholar
Lu, L. and Wu, H.C. (2012). Novel robust direction-of-arrival-based source localization algorithm for wideband signals. IEEE Trans. Wireless Communications, 11, 3850–3859.Google Scholar
Navarro, M. and Najar, M. (2011). Frequency domain joint TOA and DOA estimation in IR-UWB. IEEE Trans Wireless Communications, 10, 3174–3184.Google Scholar
Park, S. and Lee, H. (2013). Self-recognition of vehicle position using UHF passive RFID tags. IEEE Trans. Industrial Electronics, 60, 226–234.Google Scholar
Poturalski, M., Flury, M., Papadimitratos, P., Hubaux, J.P. and Le Boudec, J.Y. (2012). On secure and precise IR-UWB ranging. IEEE Trans Wireless Communications, 11, 1087–1099.CrossRefGoogle Scholar
Salman, N., Ghogho, M. and Kemp, A.H. (2012). On the joint estimation of the RSS-based location and path-loss exponent. IEEE Wireless Communications Letters, 1, 34–37.Google Scholar
Shen, J., Molisch, A.F. and Salmi, J. (2012). Accurate passive location estimation using TOA measurements. IEEE Trans Wireless Communications, 11, 2182–2192.Google Scholar
Song, H., Shin, V. and Jeon, M. (2012). Mobile node localization using fusion prediction-based interacting multiple model in Cricket sensor network. IEEE Trans. Industrial Electronics, 59, 4349–4359.Google Scholar
Sugano, M., Kawazoe, T., Ohta, Y. and Murata, M. (2006). Indoor localization system using RSSI measurement of wireless sensor networks based on ZigBee standard. Proceedings of Wireless Sensor Networks 2006 (WSN2006), Alberta, Canada.Google Scholar
Wang, G., Chen, H., Li, Y. and Jin, M. (2012). On received-signal strength based localization with unknown transmit power and path loss exponent. IEEE Wireless Communications Letters, 1, 536–539.Google Scholar
Xue, T., Shi, Y. and Dong, X. (2012). A framework for location-aware strategies in cognitive radio systems. IEEE Wireless Communications Letters, 1, 30–33.Google Scholar
Yu, H., Huang, G., Gao, J. and Liu, B. (2012). An efficient constrained weighted least squares algorithm for moving source location using TDOA and FDOA measurements. IEEE Trans. Wireless Communications, 11, 44–47.Google Scholar
Zhou, S. and Pollard, J.K. (2006). Position measurement using Bluetooth. IEEE Tans. Consumer Electronics, 52, 555–558.Google Scholar