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      KCI등재 SCIE SCOPUS

      Deep-Fading Hole Avoidance for Secure Region Detection using Channel State Information

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      https://www.riss.kr/link?id=A108425066

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      다국어 초록 (Multilingual Abstract)

      One of the critical challenges in many wirelesssystems is the deep fading hole problem where signals interferedestructively to create an abrupt change in signal amplitude dueto multipath fading. In this work, we tackle this challenge in thecontext of ...

      One of the critical challenges in many wirelesssystems is the deep fading hole problem where signals interferedestructively to create an abrupt change in signal amplitude dueto multipath fading. In this work, we tackle this challenge in thecontext of secure region detection (SRD) problem. Specifically, wepropose SHARD, a novel hole avoidance technique that analyzeschannel state information (CSI) to significantly improve theaccuracy of CSI-based SRD. SHARD identifies potential fadingholes in CSI amplitude, and removes an unreliable portion of datawhile utilizing the remaining unaffected part. To compensate forthe loss of information, we define phase-distance for reliable useof time-varying CSI phase, and neighboring reference points areutilized for accurate matching. Our real-world experiments showthat SHARD can achieve a near-perfect 99.96% true-negativeratio (successfully rejecting devices not in the secure region)and an excellent true-positive of 98.01% for practical usage,significantly better than state-of-the-art prior work. We believeour ideas can be generalized to many RF-based localizationsystems to mitigate the deep fading hole problem and improvetheir accuracy.

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      참고문헌 (Reference) 논문관계도

      1 A. Goldsmith, "Wireless communications" Cambridge Univ. Press 2005

      2 J. Niu, "WicLoc : An indoor localization system based on WiFi fingerprints and crowdsourcing" 2015

      3 C. Xu, "The case for efficient and robust RF-based device-free localization" 15 (15): 2362-2375, 2016

      4 Y. Yoo, "Secure region detection using Wi-Fi CSI and one-class classification" 9 : 65 906-65 913, 2021

      5 P. Bahl, "RADAR: An in-building RF-based user location and tracking system" 2 : 2000

      6 H. Xue, "Push the limit of multipath profiling using commodity WiFi devices With limited bandwidth" 69 (69): 4142-4154, 2020

      7 Y. Xie, "Precise power delay profiling with commodity WiFi" 2015

      8 Y. Xie, "Precise power delay profiling with commodity Wi-Fi" 18 (18): 1342-1355, 2019

      9 Mansoor Ahmed Bhatti ; Rabia Riaz ; Sana Shokat ; Farina Riaz ; Se Jin Kwon, "Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning" 한국통신학회 22 (22): 236-243, 2020

      10 M. R. Berthold, "On clustering time series using euclidean distance and pearson correlation" 2016

      1 A. Goldsmith, "Wireless communications" Cambridge Univ. Press 2005

      2 J. Niu, "WicLoc : An indoor localization system based on WiFi fingerprints and crowdsourcing" 2015

      3 C. Xu, "The case for efficient and robust RF-based device-free localization" 15 (15): 2362-2375, 2016

      4 Y. Yoo, "Secure region detection using Wi-Fi CSI and one-class classification" 9 : 65 906-65 913, 2021

      5 P. Bahl, "RADAR: An in-building RF-based user location and tracking system" 2 : 2000

      6 H. Xue, "Push the limit of multipath profiling using commodity WiFi devices With limited bandwidth" 69 (69): 4142-4154, 2020

      7 Y. Xie, "Precise power delay profiling with commodity WiFi" 2015

      8 Y. Xie, "Precise power delay profiling with commodity Wi-Fi" 18 (18): 1342-1355, 2019

      9 Mansoor Ahmed Bhatti ; Rabia Riaz ; Sana Shokat ; Farina Riaz ; Se Jin Kwon, "Outlier Detection in Indoor Localization and Internet of Things (IoT) using Machine Learning" 한국통신학회 22 (22): 236-243, 2020

      10 M. R. Berthold, "On clustering time series using euclidean distance and pearson correlation" 2016

      11 T. Watteyne, "Mitigating multipath fading through channel hopping in wireless sensor networks" 2010

      12 J. Ock, "Low-power wireless with denseness: The case of an electronic shelf labeling system—design and experience" 7 : 163 887-163 897, 2019

      13 G. Pecoraro, "LTE signal fingerprinting localization based on CSI" 2017

      14 R. C. Luo, "Indoor localization system based on hybrid Wi-Fi/BLE and hierarchical topological fingerprinting approach" 68 (68): 10 791-10 806, 2019

      15 F. R. Almeida, "Improving multidimensional wireless sensor network lifetime using pearson correlation and fractal clustering" 17 (17): 1317-, 2017

      16 Q. Chen, "FinCCM : Fingerprint crowdsourcing, clustering and matching for indoor subarea localization" 4 (4): 677-680, 2015

      17 I. Priness, "Evaluation of gene-expression clustering via mutual information distance measure" 8 (8): 1-12, 2007

      18 S. Maric, "Construction of optimal frequency hopping sequences for minimizing bit errors in selective fading channels characteristic to digital cellular systems" 1993

      19 J. Y. Song, "Cluster analysis on the structure of the cryptocurrency market via bitcoin–ethereum filtering" 527 : 121339-, 2019

      20 Q. Song, "CSI amplitude fingerprintingbased NB-IoT indoor localization" 5 (5): 1494-1504, 2018

      21 X. Zhang, "CRLB for the localization error in the presence of fading" 2013

      22 S. Huang, "COMVELOC : A compensation vectorbased indoor localization system in WIFI environments" 2018

      23 H. Xie, "Accelerating crowdsourcing based indoor localization using CSI" 2015

      24 P. Davidson, "A survey of selected indoor positioning methods for smartphones" 19 (19): 1347-1370, 2017

      25 G. J. Torres, "A similarity measure for clustering and its applications" 3 (3): 164-170, 2009

      26 X. Bao, "A novel fading-tolerant high-accuracy localization algorithm using distributed space-time block codes" 2008

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