pith. sign in

arxiv: 1907.00468 · v1 · pith:XAN4GYHWnew · submitted 2019-06-30 · 💻 cs.NI · cs.PF· cs.SE· eess.SP

A Fast-rate WLAN Measurement Tool for Improved Miss-rate in Indoor Navigation

Erick Schmidt , David Akopian This is my paper

Pith reviewed 2026-05-25 11:52 UTC · model grok-4.3

classification 💻 cs.NI cs.PFcs.SEeess.SP
keywords indoor positioningWLAN fingerprintingRSS measurementfast-rate collectionmiss rateradio map surveyingnavigation
0
0 comments X

The pith

A fast-rate WLAN RSS collection method raises the probability of obtaining measurements within each one-second window.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a technique to collect received signal strength measurements from WLAN access points at a rate higher than the conventional one per second. Standard collection leaves gaps that disrupt fingerprint-based indoor positioning during both radio-map surveying and user navigation. The fast-rate approach supplies additional samples for processing signal variations and shortens the time required to survey locations. The authors compare it to the usual rate inside a test setup built to copy real surveying and navigation conditions.

Core claim

The authors claim that a fast-rate measurement collection method on WLAN cards achieves a higher probability of RSS measurement collection during a given one-second window. This supplies more data for statistical processing and enables faster surveying of radio maps used in WLAN-based indoor positioning systems. The method is evaluated against the conventional one-Hz rate inside a testing methodology that mimics real-life IPS surveying and online navigation scenarios.

What carries the argument

The fast-rate RSS measurement collection method, which gathers multiple received signal strength samples per second instead of one at the standard 1 Hz rate.

If this is right

  • More measurements become available for statistical processing of signal fluctuations.
  • Time required for offline radio-map surveying decreases.
  • Fewer measurement misses affect fingerprint matching during both calibration and navigation.
  • Online navigation receives more frequent data points for association with the radio map.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If the method runs on unmodified consumer WLAN hardware, existing fingerprinting systems could adopt it without hardware replacement.
  • The proposed testing methodology offers a reusable template for evaluating other collection rates or signal-processing techniques under simulated real conditions.
  • Shorter surveying times could make large-scale radio-map creation for WLAN fingerprinting more feasible in practice.

Load-bearing premise

The fast-rate collection can be implemented on standard WLAN cards and the testing methodology accurately mimics real-life surveying and navigation without introducing new measurement artifacts.

What would settle it

A direct comparison experiment in which the fast-rate method fails to produce a measurably higher probability of RSS collection inside a one-second window than the conventional 1 Hz rate.

Figures

Figures reproduced from arXiv: 1907.00468 by David Akopian, Erick Schmidt.

Figure 1
Figure 1. Figure 1: shows the interaction of RSS measurements coming from WLAN packets with capturing filters inside the WLAN card. The capturing tool would then capture in so-called “normal mode”, thus limiting the capturing rate. For this mode, the capturing steps can be seen as follows: WLAN card  capturing filter  capturing tool  hard drive. The majority of the WLAN-fingerprint measurement collection solutions in liter… view at source ↗
Figure 2
Figure 2. Figure 2: WLAN physical layer packet data unit structure for beacon frames [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Apartment floorplan used in traffic test. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: shows the PDF and CDF plot to better depict this packet loss phenomena due to faulty APs for normal mode when distance is increased on the mobile device. A clear evidence of packet arrival near the 2000 ms interval (or 2 second interval) can be seen when at -80 dBm. This translates to a packet miss in the one-second window interval and can highly impact measurement availability. In addition, this observed … view at source ↗
read the original abstract

Recently, location-based services (LBS) have steered attention to indoor positioning systems (IPS). WLAN-based IPSs relying on received signal strength (RSS) measurements such as fingerprinting are gaining popularity due to proven high accuracy of their results. Typically, sets of RSS measurements at selected locations from several WLAN access points (APs) are used to calibrate the system. Retrieval of such measurements from WLAN cards are commonly at one-Hz rate. Such measurement collection is needed for offline radio-map surveying stage which aligns fingerprints to locations, and for online navigation stage, when collected measurements are associated with the radio-map for user navigation. As WLAN network is not originally designed for positioning, an RSS measurement miss could have a high impact on the fingerprinting system. Additionally, measurement fluctuations require laborious signal processing, and surveying process can be very time consuming. This paper proposes a fast-rate measurement collection method that addresses previously mentioned problems by achieving a higher probability of RSS measurement collection during a given one-second window. This translates to more data for statistical processing and faster surveying. The fast-rate collection approach is analyzed against the conventional measurement rate in a proposed testing methodology that mimics real-life scenarios related to IPS surveying and online navigation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The manuscript proposes a fast-rate WLAN measurement collection method for RSS-based indoor positioning systems (IPS) using fingerprinting. It claims this approach achieves a higher probability of collecting measurements within a given one-second window compared to the standard 1 Hz rate from WLAN cards. The method is intended to supply more data for statistical processing, reduce the impact of measurement misses, and speed up the offline radio-map surveying process, with evaluation via a testing methodology that mimics real-life IPS surveying and online navigation scenarios.

Significance. If the method can be implemented on commodity WLAN hardware and demonstrably improves collection rates without new artifacts, it would address a practical bottleneck in WLAN fingerprinting systems. However, the manuscript provides no equations, implementation details, data, error analysis, or results, so the significance cannot be assessed.

major comments (2)
  1. [Abstract] Abstract: the central claim that the fast-rate method achieves 'higher probability of RSS measurement collection' is presented without any supporting equations, algorithms, hardware modifications, or quantitative results. This absence makes the claim impossible to verify or reproduce.
  2. [Abstract] Abstract: the testing methodology is described only at a high level as 'mimicking real-life scenarios'; no details are given on how the one-second window is defined, how misses are counted, what hardware is used, or how the conventional 1 Hz baseline is implemented for fair comparison.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their review. The abstract is a concise summary; the full manuscript provides the supporting details on the method and evaluation. We respond to each major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that the fast-rate method achieves 'higher probability of RSS measurement collection' is presented without any supporting equations, algorithms, hardware modifications, or quantitative results. This absence makes the claim impossible to verify or reproduce.

    Authors: The abstract summarizes the contribution at a high level. The full manuscript presents the fast-rate collection algorithm (including how it increases RSS retrieval probability within one-second windows on commodity WLAN hardware without modifications), the underlying probability model, and quantitative results from the evaluation against the 1 Hz baseline. revision: no

  2. Referee: [Abstract] Abstract: the testing methodology is described only at a high level as 'mimicking real-life scenarios'; no details are given on how the one-second window is defined, how misses are counted, what hardware is used, or how the conventional 1 Hz baseline is implemented for fair comparison.

    Authors: The manuscript body details the testing methodology, including the definition of the one-second window aligned with typical IPS navigation timing, the miss-counting procedure based on successful multi-AP RSS retrievals, use of standard commodity WLAN cards, and the 1 Hz baseline implemented by rate-limiting collection for direct comparison. A brief pointer to these sections can be added to the abstract for improved clarity. revision: partial

Circularity Check

0 steps flagged

No circularity; empirical method proposal with no derivation chain

full rationale

The paper describes a practical fast-rate RSS collection approach for WLAN-based indoor positioning to reduce measurement misses during one-second windows. No equations, first-principles derivations, fitted parameters renamed as predictions, or load-bearing self-citations appear in the abstract or description. The contribution is an implementation and testing methodology for surveying/navigation scenarios, not a mathematical claim that reduces to its inputs. This is the common case of a non-circular engineering paper.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no equations, methods, or data are provided to identify free parameters, axioms, or invented entities.

pith-pipeline@v0.9.0 · 5747 in / 1066 out tokens · 29664 ms · 2026-05-25T11:52:10.225932+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

32 extracted references · 32 canonical work pages

  1. [1]

    and Enge, P., Global Positioning System: Signals, Measurements, and Performance , 2nd ed

    Misra, P. and Enge, P., Global Positioning System: Signals, Measurements, and Performance , 2nd ed. Ganga -Jamuna Press, Lincoln MA, 2006

    work page 2006

  2. [2]

    Modern WLAN fingerprinting indoor positioning methods and deployment challenges,

    Khalajmehrabadi, A., Gatsis, N. and Akopian, D., “Modern WLAN fingerprinting indoor positioning methods and deployment challenges,” IEEE Communication Surveys and Tutorials, Vol. 19, No. 3, 2017, pp. 1974-2002

    work page 2017

  3. [3]

    Super-resolution TOA estimation with diversity for indoor geolocation,

    Li, X. and Pahlavan, K., “Super-resolution TOA estimation with diversity for indoor geolocation,” IEEE Transactions on Wireless Communications, Vol. 3, No. 1, Jan. 2004, pp. 224–234

    work page 2004

  4. [4]

    Wireless localization using time difference of arrival in narrow- band multipath systems,

    Comsa, C.-R., Luo, J., Haimovich, A. and Schwartz, S., “Wireless localization using time difference of arrival in narrow- band multipath systems,” in International Symposium on Signals, Circuits and Systems, Vol. 2, Jul. 2007, pp. 1–4

    work page 2007

  5. [5]

    Indoor Localization Using 802.11 Time Differences of Arrival,

    Makki, A., Siddig, A., Saad, M., Cavallaro, J.R. and Bleakley, C.J., “Indoor Localization Using 802.11 Time Differences of Arrival,” IEEE Transactions on Instrumentation and Measurements, Vol.65, No.3, 2016, pp.614-623

    work page 2016

  6. [6]

    An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration technique for Internet of Things (IoT),

    Rusli, M. E., Ali, M., Jamil, N. and Din, M. M., "An Improved Indoor Positioning Algorithm Based on RSSI-Trilateration technique for Internet of Things (IoT)," in Computer and Communication Engineering (ICCCE), 2016 International Conference on, Kuala Lumpur, Malaysia, 2016

    work page 2016

  7. [7]

    INTRI: Contour -Based Trilateration for Indoor Fingerprint -Based Localization,

    He, S. and Chan, S.-H. G. , "INTRI: Contour -Based Trilateration for Indoor Fingerprint -Based Localization," IEEE Transactions on Mobile Computing, Vol. 16, No. 6, Jun. 2017, pp. 1676 - 1690

    work page 2017

  8. [8]

    Integration of angle of arrival information for multimodal sensor network localization using semidefinite programming,

    Biswas, P., Aghajan, H. and Ye, Y., “Integration of angle of arrival information for multimodal sensor network localization using semidefinite programming,” in Proceedings of the 39th Asilomar Conference on Signals, Systems and Computers , Oct. 2005

    work page 2005

  9. [9]

    Ad hoc posit ioning system (APS) using AOA,

    Niculescu, D. and Nath, B., “Ad hoc posit ioning system (APS) using AOA,” in Proceedings of the 22nd Annual Joint Conference of the IEEE Computer and Communications, Vol. 3, Mar. 2003, pp. 1734–1743

    work page 2003

  10. [10]

    Wi-Fi Fingerprint -Based Indoor Positioning: Recent Advances and Compar isons,

    He, S. and Chan, S.-H. G. , "Wi-Fi Fingerprint -Based Indoor Positioning: Recent Advances and Compar isons," IEEE Communication Surveys and Tutorials, Vol. 18, No. 1, 2016, pp. 466-490

    work page 2016

  11. [11]

    Optimized indoor wireless propagation model in WiFi -RoF network architecture for RSS -based localization in the Internet of Things,

    Shen, X., Xu, K., Sun X., Wu , J. and Lin, J., "Optimized indoor wireless propagation model in WiFi -RoF network architecture for RSS -based localization in the Internet of Things," in Microwave Photonics, 2011 International Topical Meeting on & Microwave Photonics Conference, 2011 Asia-Pacific, MWP/APMP, Singapore, Singapore, 2011

    work page 2011

  12. [12]

    An enhanced WiFi indoor localization system based on machine learning,

    Salamah, A. H., Tamazin, M., Sharkas, M. A. and Khedr, M., "An enhanced WiFi indoor localization system based on machine learning," in Indoor Positioning and Indoor Navigation (IPIN), 2016 International Conference on , Alcala de Henares, Spain, 2016

    work page 2016

  13. [13]

    CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach,

    Wang, X., Gao, L., Mao, S. and Pandey, S., "CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach," IEEE Transactions on Vehicular Technology, Vol. 66, No. 1, 2017, pp. 763-776

    work page 2017

  14. [14]

    Performance Evaluation of Radio Map Construction Methods for Wi-Fi Positioning Systems,

    Jung, S. H., Moon, B.-C. and Han, D., "Performance Evaluation of Radio Map Construction Methods for Wi-Fi Positioning Systems," IEEE Transactions on Intelligent Transportation Systems, Vol. 18, No. 4, Apr. 2017, pp. 880-889

    work page 2017

  15. [15]

    Intelligent radio map management for future WLAN indoor location fingerprinting,

    Shih, C., Chen, L., Chen, G., Wu, E. H. and Jin, M., "Intelligent radio map management for future WLAN indoor location fingerprinting," in Wireless Communications and Networking Conference (WCNC), 2012 IEEE, Shanghai, China, 2012

    work page 2012

  16. [16]

    Power-efficient access point select ion for indoor location estimation,

    Chen, Y., Yang, Q., Yin, J. and Chai, X., “Power-efficient access point select ion for indoor location estimation,” IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 7, Jul. 2006, pp. 877–888

    work page 2006

  17. [17]

    A survey of outlier detection methodologies,

    Hodge, V. and Austin, J., “A survey of outlier detection methodologies,” Artificial Intelligence Review , Vol. 22, No. 2, Oct. 2004, pp. 85–126

    work page 2004

  18. [18]

    Outlier detection technique for RSS -based localization problems in wireless sensor networks,

    Chen, Y. C., Sun, W. C. and Juang, J. C., “Outlier detection technique for RSS -based localization problems in wireless sensor networks,” in Proceedings of SICE Annual Conference, Aug. 2010, pp. 657–662. 9

    work page 2010

  19. [19]

    A joint indoor WLAN localization and outlier detection scheme using LASSO and Elastic-Net optimization techniques,

    Khalajmehrabadi, A., Gatsis, N., Pack, D. and Akopian, D., “A joint indoor WLAN localization and outlier detection scheme using LASSO and Elastic-Net optimization techniques,” IEEE Transactions on Mobile Computing, Vol. 16, No.8, 2017, pp. 2079 - 2092

    work page 2017

  20. [20]

    Faulty measurements impact on wireless local area network positioning performance,

    Morales, J. A., Akopian, D. and Agaian, S., “Faulty measurements impact on wireless local area network positioning performance,” IET Radar, Sonar & Navigation, Vol. 9, No. 5, Aug. 2015, pp. 501–508

    work page 2015

  21. [21]

    Mitigating anomalous measurements for indoor wireless local area network positioning,

    Morales, J. A., Akopian, D. and Agaian, S., “Mitigating anomalous measurements for indoor wireless local area network positioning,” IET Radar, Sonar & Navigation, Vol. 10, No. 7, 2016, pp. 1220-1227

    work page 2016

  22. [22]

    CaptureSetup/WLAN - The Wireshark Wiki,

    Wireshark, "CaptureSetup/WLAN - The Wireshark Wiki," Wireshark, [Online]. Available: https://wiki.wireshark.org/CaptureSetup/WLAN. [Accessed May 2017]

    work page 2017

  23. [23]

    Fault Detection and Mitigation in WLAN RSS Fingerprint -based Positioning,

    Laoudias, C., Michaelides, M. P., Panayiotou, C. G., “Fault Detection and Mitigation in WLAN RSS Fingerprint -based Positioning,” in Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conf erence on , Guimaraes, Portugal, Sept. 2011

    work page 2011

  24. [24]

    Kernel-Based Positioning in Wireless Local Area Network s,

    Kushki, A., Plataniotis, K. N., Venetsanopoulos, A. N., “Kernel-Based Positioning in Wireless Local Area Network s,” IEEE Transactions on Mobile Computing, Vol. 6, No. 6, Jun. 2007, pp. 689-705

    work page 2007

  25. [25]

    A Performance Study of a Fast -Rate WLAN Fingerprinting Measurement Collection Method,

    Schmidt, E., Moham med, M. A. and Akopian, D., “A Performance Study of a Fast -Rate WLAN Fingerprinting Measurement Collection Method,” IEEE Transactions on Instrumentation and Measurements, Vol. 67, No. 10, Oct. 2018, pp. 2273–2281

    work page 2018

  26. [26]

    Distance-based interpolation and extrapolation methods for RSS -based localization with indoor wireless signals,

    Talvitie, J.., Renfors, M. and Lohan, E. S., “Distance-based interpolation and extrapolation methods for RSS -based localization with indoor wireless signals,” IEEE Transactions on Vehicular Technology, Vol. 64, No. 4, Apr. 2015, pp. 1340–1353

    work page 2015

  27. [27]

    The effect of coverage gaps and measurement inaccuracies in fingerprinting based indoor localization,

    Talvitie, J., Lohan, E. S., Renfors, M., “The effect of coverage gaps and measurement inaccuracies in fingerprinting based indoor localization,” in Localization and GNSS (ICL-GNSS), 2014 International Conference on, Helsinki, Finland, 2014

    work page 2014

  28. [28]

    IEEE Standard 802.11 -2012 - Part 11: Wireless LAN Medium Access Cont rol (MAC) and Physical Layer (PHY) Specifications, IEEE Standards Association, The Institute of Electrical and Electronics Engineers, Inc, 2012

    work page 2012

  29. [29]

    Available: http://aircrack-ng.org/

    Aircrack-ng, Aircrack-ng, [Online]. Available: http://aircrack-ng.org/. [Accessed December 2016]

    work page 2016

  30. [30]

    Free WiFi scanner and channel scanner for windows, [Online]

    Acrylic WiFi . Free WiFi scanner and channel scanner for windows, [Online]. Available: https://www.acrylicwifi.com/en/wlan-software/wlan-scanner-acrylic-wifi-free/. [Accessed December 2016]

    work page 2016

  31. [31]

    Jam Software, [Online]

    Jam Software, HeavyLoad - Free Stress Tool for Your PC. Jam Software, [Online] . Available: https://www.jam - software.com/heavyload/. [Accessed Decemeber 2016]

    work page 2016

  32. [32]

    Available: http://kernel.ubuntu.com/~cking/stress -ng/

    Ubuntu Kernel Team, stress -ng, [Online]. Available: http://kernel.ubuntu.com/~cking/stress -ng/. [Accessed December 2016]

    work page 2016