short-paper

Mining channel state information from bluetooth low energy RSSI for robust object-to-object ranging

Authors:

Rahul Majethia,

Krishnan RajkumarAuthors Info & Claims

IOT '18: Proceedings of the 8th International Conference on the Internet of Things

Article No.: 23, Pages 1 - 4

https://doi.org/10.1145/3277593.3277624

Published: 15 October 2018 Publication History

Get Access

Abstract

In the world of smart objects, device-ranging and localization using Bluetooth Low Energy (BLE) is becoming popular due to its attractive energy performance, wide platform support and low costs. There has been sufficient motivation on statistical analysis of Channel State Information of Received Signal Strength Indicator (RSSI) data for more effective ranging-based models. However, there has been no ubiquitous solution which is both receiver-agnostic and does not require alteration in the advertisement protocol or packet structure of BLE. In this paper, we propose a truly unsupervised approach for channel-annotation of RSSI data received by a stationary receiver object. Given a sequence of RSSI observations and a discoverable receiver channel-switching policy, we determine the period and hence the time spent by the receiver in an individual channel. Then, we propose a sliding-window based algorithm which utilizes two well-established Likelihood-Ratio algorithms - KLIEP and uLSIF - for extracting Channel State Information of retrospective RSSI observation data. We believe this work lays the foundation of motivating future work in completely unsupervised methods for object-to-object ranging and localization.

References

[1]

Samaneh Aminikhanghahi and Diane J Cook. 2017. A survey of methods for time series change point detection. Knowledge and information systems 51, 2 (2017), 339--367.

Digital Library

Google Scholar

[2]

Gabriel de Blasio, Alexis Quesada-Arencibia, Carmelo R. García, Roberto Moreno-Díaz, and Jose Carlos Rodríguez-Rodríguez. 2017. Analysis of Distance and Similarity Metrics in Indoor Positioning Based on Bluetooth Low Energy. In Ubiquitous Computing and Ambient Intelligence. Springer International Publishing, 213--224.

Google Scholar

[3]

R. Faragher and R. Harle. 2015. Location Fingerprinting With Bluetooth Low Energy Beacons. IEEE Journal on Selected Areas in Communications 33, 11 (Nov 2015), 2418--2428.

Digital Library

Google Scholar

[4]

Shohei Hido, Yuta Tsuboi, Hisashi Kashima, Masashi Sugiyama, and Takafumi Kanamori. 2011. Statistical outlier detection using direct density ratio estimation. Knowledge and information systems 26, 2 (2011), 309--336.

Google Scholar

[5]

Shigemi Ishida, Yoko Takashima, Shigeaki Tagashira, and Akira Fukuda. 2016. Proposal of Separate Channel Fingerprinting Using Bluetooth Low Energy. In Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on. IEEE, 230--233.

Crossref

Google Scholar

[6]

Kang Eun Jeon, James She, Perm Soonsawad, and Pai Chet Ng. 2018. BLE Beacons for Internet of Things Applications: Survey, Challenges and Opportunities. IEEE Internet of Things Journal (2018).

Google Scholar

[7]

Takafumi Kanamori, Shohei Hido, and Masashi Sugiyama. 2009. A least-squares approach to direct importance estimation. Journal of Machine Learning Research 10, Jul (2009), 1391--1445.

Digital Library

Google Scholar

[8]

Alessandro Montanari, Sarfraz Nawaz, Cecilia Mascolo, and Kerstin Sailer. 2017. A Study of Bluetooth Low Energy performance for human proximity detection in the workplace. In Pervasive Computing and Communications (PerCom), 2017 IEEE International Conference on. IEEE, 90--99.

Crossref

Google Scholar

[9]

Masashi Sugiyama, Shinichi Nakajima, Hisashi Kashima, Paul V Buenau, and Motoaki Kawanabe. 2008. Direct importance estimation with model selection and its application to covariate shift adaptation. In Advances in neural information processing systems. 1433--1440.

Digital Library

Google Scholar

Cited By

View all

Dixit A(2023)An AI based Smart Conference Calling System using Bluetooth Technology2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON59035.2023.10316058(613-618)Online publication date: 12-Oct-2023
https://doi.org/10.1109/UEMCON59035.2023.10316058
Gong MLi ZLi W(2022)Research on ultra-wideband (UWB) indoor accurate positioning technology under signal interference2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00060(147-154)Online publication date: Aug-2022
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00060
Marek DGrochla KDuda J(2021)Channel State Information Variability in BLE Positioning MeasurementsAdvances in Systems Engineering10.1007/978-3-030-92604-5_26(293-300)Online publication date: 11-Dec-2021
https://doi.org/10.1007/978-3-030-92604-5_26
Show More Cited By

Recommendations

Tracking a moving user in indoor environments using Bluetooth low energy beacons
Graphical abstract

Display Omitted
Highlights
- RSSI-based location tracking approaches require a laborious calibration process.
Abstract Background
Bluetooth low energy (BLE) beacons have been used to track the locations of individuals in indoor environments for clinical applications such as workflow analysis and infectious disease modelling. Most current ...
Exact Error Rate Analysis of MIMO-MRC System under Cochannel Interference and Imperfect Channel State Information

In this paper, we derive closed-form solution for the bit error rate of multi-input multi-output (MIMO) system with maximum ratio combining. We consider binary PSK modulation suffers from cochannel interference (CCI) and imperfect channel state ...
NLOS identification and mitigation based on channel state information for indoor WiFi localisation

Indoor localisation could benefit greatly from non‐line‐of‐sight (NLOS) identification and mitigation, since the major challenge for WiFi indoor ranging‐based localisation technologies is multipath and NLOS. NLOS identification and mitigation on commodity ...

Comments

Information & Contributors

Information

Published In

IOT '18: Proceedings of the 8th International Conference on the Internet of Things

October 2018

299 pages

ISBN:9781450365642

DOI:10.1145/3277593

Conference Chairs:
Krzysztof Janowicz
University of California, Santa Barbara
,
Werner Kuhn
University of California, Santa Barbara
,
Program Chairs:
Federica Cena
University of Torino, Italy
,
Armin Haller
Australian National University, Australia
,
Kyriakos G. Vamvoudakis
Georgia Institute of Technology, Georgia Tech

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 October 2018

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

IOT '18

IOT '18: 8th International Conference on the Internet of Things

October 15 - 18, 2018

California, Santa Barbara, USA

Acceptance Rates

Overall Acceptance Rate 28 of 84 submissions, 33%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
200
Total Downloads

Downloads (Last 12 months)8
Downloads (Last 6 weeks)2

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Dixit A(2023)An AI based Smart Conference Calling System using Bluetooth Technology2023 IEEE 14th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON)10.1109/UEMCON59035.2023.10316058(613-618)Online publication date: 12-Oct-2023
https://doi.org/10.1109/UEMCON59035.2023.10316058
Gong MLi ZLi W(2022)Research on ultra-wideband (UWB) indoor accurate positioning technology under signal interference2022 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00060(147-154)Online publication date: Aug-2022
https://doi.org/10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics55523.2022.00060
Marek DGrochla KDuda J(2021)Channel State Information Variability in BLE Positioning MeasurementsAdvances in Systems Engineering10.1007/978-3-030-92604-5_26(293-300)Online publication date: 11-Dec-2021
https://doi.org/10.1007/978-3-030-92604-5_26
Zhang HUddin MHao FMukherjee SMohapatra PWolman AZhong L(2019)AIDEProceedings of the 20th International Workshop on Mobile Computing Systems and Applications10.1145/3301293.3302354(123-128)Online publication date: 22-Feb-2019
https://dl.acm.org/doi/10.1145/3301293.3302354
Majethia R(2018)Deep Context Mining of Individuals and Groups Using Smartphone Sensor and Usage DataProceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers10.1145/3267305.3277808(1750-1753)Online publication date: 8-Oct-2018
https://dl.acm.org/doi/10.1145/3267305.3277808

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Recommendations

Tracking a moving user in indoor environments using Bluetooth low energy beacons

Exact Error Rate Analysis of MIMO-MRC System under Cochannel Interference and Imperfect Channel State Information

NLOS identification and mitigation based on channel state information for indoor WiFi localisation

Comments

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

Abstract

References

Cited By

Recommendations

Tracking a moving user in indoor environments using Bluetooth low energy beacons

Exact Error Rate Analysis of MIMO-MRC System under Cochannel Interference and Imperfect Channel State Information

NLOS identification and mitigation based on channel state information for indoor WiFi localisation

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations