More Web Proxy on the site http://driver.im/

research-article

Sample Size Determination Algorithm for fingerprint-based indoor localization systems

Authors:

Loizos Kanaris,

Giancarlo Fortino,

Antonio Liotta,

Stavros StavrouAuthors Info & Claims

Computer Networks: The International Journal of Computer and Telecommunications Networking, Volume 101, Issue C

Pages 169 - 177

https://doi.org/10.1016/j.comnet.2015.12.015

Published: 04 June 2016 Publication History

Abstract

Provision of accurate location information is an important task in the Internet of Things (IoT) applications and scenarios. This need has boosted the research and development of fingerprint based, indoor localization systems, since GPS information is not available in indoor environments. Performance evaluation of such systems and their related localization algorithms, is usually based on sampling collection in predetermined test environments. The sample size determination and sampling methodology can significantly affect the reliability of the outcome. This work proposes an algorithm that calculates the minimum sample size of positioning data required for objective performance evaluation of fingerprint based localization systems. The use of a correct, independent, unbiased and representative sample size can speed up the training, evaluation and calibration procedures of a fingerprint based localization system, while ensuring that the system's true accuracy is achieved. The proposed Sample Size Determination Algorithm (SSDA) takes into consideration the desired confidence level, the resulting standard deviation of a small size preliminary sample as well as the error approximation with respect to the actual error of the system and proposes the final sample size for the evaluation and/or calibration and/or training of the utilized radio-maps. Additionally, the SSDA, assumes random sample allocation in the area of interest in order to avoid biased results. Risks arising from the selection of a sample of convenience are also investigated. Finally, the performance of the proposed algorithm is tested in both measured and simulated radio-maps.

References

[1]

M. Chen, Towards smart city: M2m communications with software agent intelligence, Multimed. Tools Appl., 67 (2013) 167-178.

[2]

B.C. David S Moore, G.P. MCCabe, W. H. Freeman, 2012.

[3]

T. Deasy, W. Scanlon, Simulation or measurement: The effect of radio map creation on indoor WLAN-Based localisation accuracy, Wirel. Pers. Commun., 42 (2007) 563-573.

Digital Library

[4]

K. El-Kafrawy, M. Youssef, A. El-Keyi, A. Naguib, Propagation modeling for accurate indoor WLAN rss-based localization, 2010.

[5]

S.-H. Fang, C.-H. Wang, A dynamic hybrid projection approach for improved wi-fi location fingerprinting, IEEE Trans. Veh. Technol., 60 (2011) 1037-1044.

[6]

A. Hatami, K. Pahlavan, Comparative statistical analysis of indoor positioning using empirical data and indoor radio channel models, IEEE, 2006.

[7]

S. Ito, N. Kawaguchi, Data correction method using ideal wireless LAN model in positioning system, 2006.

[8]

J. Jemai, R. Piesiewicz, T. Kurner, Calibration of an indoor radio propagation prediction model at 2.4 GHz by measurements of the IEEE 802.11b preamble, 2005.

[9]

H. Ji, L. Xie, C. Wang, Y. Yin, S. Lu, Crowdsensing: A crowd-sourcing based indoor navigation using rfid-based delay tolerant network, J. Netw. Comput. Appl., 52 (2015) 79-89.

Digital Library

[10]

L. Kanaris, A. Kokkinis, A. Liotta, M. Raspopoulos, S. Stavrou, A binomial distribution approach for the evaluation of indoor positioning systems, Casablanca, Morocco, 2013.

[11]

A. Kokkinis, M. Raspopoulos, L. Kanaris, A. Liotta, S. Stavrou, Map-aided fingerprint-based indoor positioning, 2013.

[12]

C. Laoudias, M. Michaelides, C. Panayiotou, Fault tolerant positioning using WLAN signal strength fingerprints, 2010.

[13]

B. Li, J. Salter, A.G. Dempster, C. Rizos, Indoor positioning techniques based on wireless lan, 2006.

[14]

D. Macagnano, G. Destino, G. Abreu, Indoor positioning: A key enabling technology for IoT applications, 2014.

[15]

D. Macagnano, G. Destino, G. Abreu, Localization with heterogeneous information, 2014.

[16]

W. Navidi, McGraw-Hill, 2008.

[17]

M. Raspopoulos, C. Laoudias, L. Kanaris, A. Kokkinis, C. Panayiotou, S. Stavrou, 3d ray tracing for device-independent fingerprint-based positioning in WLANs, 2012.

[18]

M. Raspopoulos, C. Laoudias, L. Kanaris, A. Kokkinis, C. Panayiotou, S. Stavrou, Cross device fingerprint-based positioning using 3D ray tracing, Limassol, Cyprus, 2012.

[19]

T. Roos, P. Myllymäi, H. Tirri, P. Misikangas, J. Sievänen, A probabilistic approach to WLAN user location estimation, Int. J. Wirel. Inf. Netw., 9 (2002) 155-164.

[20]

S. Sorour, Y. Lostanlen, S. Valaee, Rss based indoor localization with limited deployment load, 2012.

[21]

S. Sorour, Y. Lostanlen, S. Valaee, K. Majeed, Joint indoor localization and radio map construction with limited deployment load, IEEE Trans. Mob. Comput., 14 (2014) 1031-1043.

Digital Library

[22]

S. Stavrou, S. Saunders, Review of constitutive parameters of building materials, IET, 2003.

[23]

M. Wallbaum, S. Diepolder, Benchmarking wireless lan location systems wireless LAN location systems, 2005.

[24]

B. Wang, S. Zhou, L.T. Yang, Y. Mo, Indoor positioning via subarea fingerprinting and surface fitting with received signal strength, Pervasive Mob. Comput., 23 (2015) 43-58.

Digital Library

[25]

H. Wang, A. Szabo, J. Bamberger, D. Brunn, U. Hanebeck, Performance comparison of nonlinear filters for indoor WLAN positioning, 2008.

[26]

Widyawan, M. Klepal, D. Pesch, Influence of predicted and measured fingerprint on the accuracy of rssi-based indoor location systems, 2007.

[27]

M. Youssef, A. Agrawala, The Horus WLAN location determination system, ACM, New York, NY, USA, 2005.

[28]

D. Zhang, L. Yang, M. Chen, S. Zhao, M. Guo, Y. Zhang, Real-time locating systems using active RFID for internet of things, Syst. J. IEEE, PP (2014) 1-10.

Cited By

xiaomeng Lxiangyu Lzhe Y(2021)A Theoretical Analysis Based on Kullback-Leibler Divergence in Sampling Size for WiFi Fingerprint-based LocalizationProceedings of the 2021 9th International Conference on Communications and Broadband Networking10.1145/3456415.3456455(244-249)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1145/3456415.3456455
Al-Ghaili AKasim HOmar RHassan ZAl-Hada NWang J(2021)Algebraic Operations-Based Secret-Key Design for Encryption Algorithm (ASKEA) for Energy Informatics and Smart Internet of Things (IoT) ApplicationsAdvances in Visual Informatics10.1007/978-3-030-90235-3_51(587-599)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-90235-3_51
Sergiou CPaphitis AKanaris LStavrou SManolopoulos YPapadopoulos GStassopoulou ADionysiou IKyriakides ITsapatsoulis N(2019)An experimental study of IoT transmission power, in outdoor environment, using Crossbow TelosB nodesProceedings of the 23rd Pan-Hellenic Conference on Informatics10.1145/3368640.3368649(128-133)Online publication date: 28-Nov-2019
https://dl.acm.org/doi/10.1145/3368640.3368649
Show More Cited By

Recommendations

A sensor based indoor localization through fingerprinting

Fingerprint based localization mostly considers to exploit existing infrastructure (APs or FM broadcast) to avoid hardware requirement and deployment cost. On the other hand these solutions confine them with such fixed infrastructures without having any ...
Reference node placement and selection algorithm based on trilateration for indoor sensor networks

The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two-phase ...
Direct-path based fingerprint extraction algorithm for indoor localization
MobiQuitous '18: Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

At present, there has been a booming interest in utilizing Channel State Information (CSI) extracted from MIMO-OFDM PHY layer to achieve precise indoor localization. Compared with Received Signal Strength Indicator (RSSI), CSI as a fine-grained feature ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Computer Networks: The International Journal of Computer and Telecommunications Networking

Computer Networks: The International Journal of Computer and Telecommunications Networking Volume 101, Issue C

June 2016

202 pages

ISSN:1389-1286

Issue’s Table of Contents

Copyright © Elsevier B.V.

Publisher

Elsevier North-Holland, Inc.

United States

Publication History

Published: 04 June 2016

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 13 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

xiaomeng Lxiangyu Lzhe Y(2021)A Theoretical Analysis Based on Kullback-Leibler Divergence in Sampling Size for WiFi Fingerprint-based LocalizationProceedings of the 2021 9th International Conference on Communications and Broadband Networking10.1145/3456415.3456455(244-249)Online publication date: 25-Feb-2021
https://dl.acm.org/doi/10.1145/3456415.3456455
Al-Ghaili AKasim HOmar RHassan ZAl-Hada NWang J(2021)Algebraic Operations-Based Secret-Key Design for Encryption Algorithm (ASKEA) for Energy Informatics and Smart Internet of Things (IoT) ApplicationsAdvances in Visual Informatics10.1007/978-3-030-90235-3_51(587-599)Online publication date: 23-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-90235-3_51
Sergiou CPaphitis AKanaris LStavrou SManolopoulos YPapadopoulos GStassopoulou ADionysiou IKyriakides ITsapatsoulis N(2019)An experimental study of IoT transmission power, in outdoor environment, using Crossbow TelosB nodesProceedings of the 23rd Pan-Hellenic Conference on Informatics10.1145/3368640.3368649(128-133)Online publication date: 28-Nov-2019
https://dl.acm.org/doi/10.1145/3368640.3368649
Zhang XYin GQi N(2019)Research on high-resolution improved projection 3D localization algorithm and precision assembly of parts based on virtual realityNeural Computing and Applications10.1007/s00521-018-3665-031:1(103-111)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s00521-018-3665-0
Alaa MZaidan AZaidan BTalal MKiah M(2017)A review of smart home applications based on Internet of ThingsJournal of Network and Computer Applications10.1016/j.jnca.2017.08.01797:C(48-65)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.jnca.2017.08.017

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents