Journal Home > Volume 3 , Issue 3
Big Data Mining and Analytics 2020, 3(3): 155-170 https://doi.org/10.26599/BDMA.2020.9020001
Open Access | Issue | Published: 16 July 2020

Error Data Analytics on RSS Range-Based Localization

Show Author's Information Shuhui Yang( ),  Zimu Yuan,  Wei Li
Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323, USA.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100864, China.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100864, China.
Keywords:
Received Signal Strength (RSS), Cramér-Rao Lower Bound (CRLB), error data analytics, generalized least squares
Cite this article:
Yang S, Yuan Z, Li W. Error Data Analytics on RSS Range-Based Localization. Big Data Mining and Analytics, 2020, 3(3): 155-170. https://doi.org/10.26599/BDMA.2020.9020001
Download citation
EndNote(RIS)
BibTeX

950

Views

50

Downloads

Citations

9

Crossref

9

WoS

10

Scopus

0

CSCD

Abstract Full text About this article

The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data. In this proposed work, we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cramér-Rao Lower Bound (CRLB). Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.


menu
Abstract
Full text
Outline
About this article

Error Data Analytics on RSS Range-Based Localization

Show Author's information Shuhui Yang( ), Zimu Yuan, Wei Li
Department of Mathematics, Statistics, and Computer Science, Purdue University Northwest, Hammond, IN 46323, USA.
Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100864, China.
Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100864, China.

Abstract

The quality of measurement data is critical to the accuracy of both outdoor and indoor localization methods. Due to the inevitable measurement error, the analytics on the error data is critical to evaluate localization methods and to find the effective ones. For indoor localization, Received Signal Strength (RSS) is a convenient and low-cost measurement that has been adopted in many localization approaches. However, using RSS data for localization needs to solve a fundamental problem, that is, how accurate are these methods? The reason of the low accuracy of the current RSS-based localization methods is the oversimplified analysis on RSS measurement data. In this proposed work, we adopt a generalized measurement model to find optimal estimators whose estimated error is equal to the Cramér-Rao Lower Bound (CRLB). Through mathematical techniques, the key factors that affect the accuracy of RSS-based localization methods are revealed, and the analytics expression that discloses the proportional relationship between the localization accuracy and these factors is derived. The significance of our discovery has two folds: First, we present a general expression for localization error data analytics, which can explain and predict the accuracy of range-based localization algorithms; second, the further study on the general analytics expression and its minimum can be used to optimize current localization algorithms.

Keywords: Received Signal Strength (RSS), Cramér-Rao Lower Bound (CRLB), error data analytics, generalized least squares

References(43)

[1]
M. Hata, Empirical formula for propagation loss in land mobile radio services, IEEE Transactions on Vehicular Technology, vol. 29, no. 3, pp. 317-325, 1980.
DOI Google Scholar
[2]
Q. Zhou and Z. S. Duan, Weighted intersections of bearing lines for AOA based localization, in 2014 17th Int. Conf. Information Fusion, Salamanca, Spain, 2014, pp. 1-8.
[3]
K. H. Yang, G. Wang, and Z. Q. Luo, Efficient convex relaxation methods for robust target localization by a sensor network using time differences of arrivals, IEEE Transactions on Signal Processing, vol. 57, no. 7, pp. 2775-2784, 2009.
DOI Google Scholar
[4]
R. M. Vaghefi and R. M. Buehrer, Asynchronous time-of-arrival-based source localization, in 2013 IEEE Int. Conf. Acoustics, Speech and Signal Processing, Vancouver, Canada, 2013, pp. 4086-4090.
DOI
[5]
Z. M. Yuan, S. H. Yang, and W. Li, A two-tier positioning algorithm for wireless networks with diverse measurement types, in 2013 IEEE Global Communications Conf., Atlanta, GA, USA, 2013, pp. 134-139.
[6]
W. Li, D. Li, S. H. Yang, Z. W. Xu, and W. Zhao, Design and analysis of a new GPS algorithm, in 2010 IEEE 30th Int. Conf. Distributed Computing Systems, Genova, Italy, 2010, pp. 40-51.
DOI
[7]
W. Li, Z. M. Yuan, B. Chen, and W. Zhao, Performance comparison of positioning algorithms for complex GPS systems, in 2012 32nd Int. Conf. Distributed Computing Systems Workshops, Macau, China, 2012, pp. 273-278.
DOI
[8]
G. Chandrasekaran, M. A. Ergin, J. Yang, S. Liu, Y. Y. Chen, M. Gruteser, and R. P. Martin, Empirical evaluation of the limits on localization using signal strength, in 2009 6th Annual IEEE Communications Society Conf. Sensor, Mesh and Ad Hoc Communications and Networks, Rome, Italy, 2009, pp. 1-9.
DOI
[9]
A. Zanella, Best practice in RSS measurements and ranging, IEEE Communications Surveys & Tutorials, vol. 18, no. 4, pp. 2662-2686, 2016.
DOI Google Scholar
[10]
E. Elnahrawy, X. Y. Li, and R. P. Martin, The limits of localization using signal strength: A comparative study, in 2004 1st Annu. IEEE Communications Society Conf. Sensor and Ad Hoc Communications Networks, Santa Clara, CA, USA, 2004, pp. 406-414.
[11]
Z. M. Yuan, W. Li, J. D. Zhu, and W. Zhao, A cost-efficiency method on beacon nodes placement for wireless localization, in 2015 Int. Conf. Computing, Networking and Communications, Garden Grove, CA, USA, 2015, pp. 546-550.
DOI
[12]
T. K. Sarkar, Z. Ji, K. Kim, A. Medouri, and M. Salazar-Palma, A survey of various propagation models for mobile communication, IEEE Antennas and Propagation Magazine, vol. 45, no. 3, pp. 51-82, 2003.
DOI Google Scholar
[13]
S. Phaiboon, An empirically based path loss model for indoor wireless channels in laboratory building, in 2002 IEEE Region 10 Conf. Computers, Communications, Control and Power Engineering, Beijing, China, 2002, pp. 1020-1023.
[14]
S. S. Ghassemzadeh, R. Jana, C. W. Rice, W. Turin, and V. Tarokh, A statistical path loss model for in-home UWB channels, in 2002 IEEE Conf. Ultra Wideband Systems and Technologies, Baltimore, MD, USA, 2002, pp. 59-64.
[15]
S. P. Singh and S. C. Sharma, Range free localization techniques in wireless sensor networks: A review, Procedia Computer Science, vol. 57, pp. 7-16, 2015.
DOI Google Scholar
[16]
B. Dil, S. Dulman, and P. Havinga, Range-based localization in mobile sensor networks, in European Workshop on Wireless Sensor Networks, K. Römer, H. Karl, and F. Mattern, eds. Berlin, Germany: Springer, 2006, pp. 164-179.
DOI
[17]
D. Denkovski, M. Angjelicinoski, V. Atanasovski, and L. Gavrilovska, Practical assessment of RSS-based localization in indoor environments, in MILCOM 2012-2012 IEEE Military Communications Conf., Orlando, FL, USA, 2012, pp. 1-6.
DOI
[18]
Y. Shang, W. Ruml, Y. Zhang, and M. P. Fromherz, Localization from mere connectivity, in Proc. 4th ACM Int. Symp. Mobile ad Hoc Networking & Computing, Annapolis, MD, USA, 2003, pp. 201-212.
DOI
[19]
L. Doherty, K. S. J. Pister, and L. El Ghaoui, Convex position estimation in wireless sensor networks, in 20th Annu. Joint Conf. IEEE Computer and Communications Societies, Anchorage, AK, USA, 2001, pp. 1655-1663.
[20]
S. Tomic, M. Beko, and R. Dinis, RSS-based localization in wireless sensor networks using convex relaxation: Noncooperative and cooperative schemes, IEEE Transactions on Vehicular Technology, vol. 64, no. 5, pp. 2037-2050, 2015.
DOI Google Scholar
[21]
L. X. Lin, H. C. So, and Y. T. Chan, Accurate and simple source localization using differential received signal strength, Digital Signal Processing, vol. 23, no. 3, pp. 736-743, 2013.
DOI Google Scholar
[22]
H. C. So and L. X. Lin, Linear least squares approach for accurate received signal strength based source localization, IEEE Transactions on Signal Processing, vol. 59, no. 8, pp. 4035-4040, 2011.
DOI Google Scholar
[23]
N. Salman, M. Ghogho, and A. H. Kemp, Optimized low complexity sensor node positioning in wireless sensor networks, IEEE Sensors Journal, vol. 14, no. 1, pp. 39-46, 2014.
DOI Google Scholar
[24]
M. R. Gholami, R. M. Vaghefi, and E. G. Ström, RSS-based sensor localization in the presence of unknown channel parameters, IEEE Transactions on Signal Processing, vol. 61, no. 15, pp. 3752-3759, 2013.
DOI Google Scholar
[25]
R. M. Vaghefi, M. R. Gholami, R. M. Buehrer, and E. G. Strom, Cooperative received signal strength-based sensor localization with unknown transmit powers, IEEE Transactions on Signal Processing, vol. 61, no. 6, pp. 1389-1403, 2013.
DOI Google Scholar
[26]
Y. M. Xu, J. G. Zhou, and P. Zhang, RSS-based source localization when path-loss model parameters are unknown, IEEE Communications Letters, vol. 18, no. 6, pp. 1055-1058, 2014.
DOI Google Scholar
[27]
S. Y. Cho, Localization of the arbitrary deployed APs for indoor wireless location-based applications, IEEE Transactions on Consumer Electronics, vol. 56, no. 2, pp. 532-539, 2010.
DOI Google Scholar
[28]
G. Wang, H. Chen, Y. M. Li, and M. Jin, On received-signal-strength based localization with unknown transmit power and path loss exponent, IEEE Wireless Communications Letters, vol. 1, no. 5, pp. 536-539, 2012.
DOI Google Scholar
[29]
M. Veletić and M. Šunjevarić, On the Cramer-Rao lower bound for RSS-based positioning in wireless cellular networks, AEU-International Journal of Electronics and Communications, vol. 68, no. 8, pp. 730-736, 2014.
DOI Google Scholar
[30]
J. Gribben and A. Boukerche, Location error estimation in wireless ad hoc networks, Ad Hoc Networks, vol. 13, pp. 504-515, 2014.
DOI Google Scholar
[31]
N. Salman, M. Ghogho, and A. H. Kemp, On the joint estimation of the RSS-based location and path-loss exponent, IEEE Wireless Communications Letters, vol. 1, no. 1, pp. 34-37, 2012.
DOI Google Scholar
[32]
X. R. Li, RSS-based location estimation with unknown pathloss model, IEEE Transactions on Wireless Communications, vol. 5, no. 12, pp. 3626-3633, 2006.
DOI Google Scholar
[33]
S. Tomic, M. Beko, R. Dinis, G. Dimic, and M. Tuba, Distributed RSS-based localization in wireless sensor networks with node selection mechanism, in Technological Innovation for Cloud-Based Engineering Systems, L. Camarinha-Matos, T. Baldissera, G. Di Orio, and F. Marques, eds. Costa de Caparica, Portugal: Springer International Publishing, 2015, pp. 204-214.
[34]
Z. M. Yuan, W. Li, A. C. Champion, and W. Zhao, An efficient hybrid localization scheme for heterogeneous wireless networks, in 2012 IEEE Global Communications Conf., Anaheim, CA, USA, 2012, pp. 372-378.
[35]
S. Tomic, M. Beko, and D. Rui, Distributed RSS-AoA based localization with unknown transmit powers, IEEE Wireless Communications Letters, vol. 5, no. 4, pp. 392-395, 2016.
DOI Google Scholar
[36]
N. Salman, Y. J. Guo, A. H. Kemp, and M. Ghogho, Analysis of linear least square solution for RSS based localization, in 2012 Int. Symp. Communications and Information Technologies, Gold Coast, Australia, 2012, pp. 1051-1054.
DOI
[37]
A. J. Weiss and J. S. Picard, Network localization with biased range measurements, IEEE Transactions on Wireless Communications, vol. 7, no. 1, pp. 298-304, 2008.
DOI Google Scholar
[38]
N. Patwari, A. O. Hero, M. Perkins, N. S. Correal, and R. J. O’Dea, Relative location estimation in wireless sensor networks, IEEE Transactions on Signal Processing, vol. 51, no. 8, pp. 2137-2148, 2003.
DOI Google Scholar
[39]
L. Mailaender, On the CRLB scaling law for received signal strength (RSS) geolocation, in 2011 45th Annu. Conf. Information Sciences and Systems, Baltimore, MD, USA, 2011, pp. 1-6.
DOI
[40]
P. Tarrío, A. M. Bernardos, and J. R. Casar, Weighted least squares techniques for improved received signal strength based localization, Sensors, vol. 11, no. 9, pp. 8569-8592, 2011.
DOI Google Scholar
[41]
J. Werner, J. Wang, A. Hakkarainen, D. Cabric, and M. Valkama, Performance and Cramer-Rao bounds for DoA/RSS estimation and transmitter localization using sectorized antennas, IEEE Transactions on Vehicular Technology, vol. 65, no. 5, pp. 3255-3270, 2016.
DOI Google Scholar
[42]
G. Casella and R. L. Berger, Statistical Inference, 2nd ed. Pacific Grove, CA, USA: Duxbury, 2002.
[43]
C. Kanzow, N. Yamashita, and M. Fukushima, WITHDRAWN: Levenberg-Marquardt methods with strong local convergence properties for solving nonlinear equations with convex constraints, Journal of Computational and Applied Mathematics, vol. 173, no. 2, pp. 321-343, 2005.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 31 January 2020
Accepted: 07 February 2020
Published: 16 July 2020
Issue date: September 2020

Copyright

© The author(s) 2020

Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (No. 2016YFE0121800).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return