Klus, Lucie; Klus, Roman; Torres-Sospedra, Joaquín; Lohan, Elena Simona; Granell-Canut, Carlos; Nurmi, Jari EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets Journal Article IEEE Transactions on Mobile Computing, 25 (5), pp. 3589-3604, 2024, ISSN: 1558-0660. Abstract | Links | BibTeX @article{Klus2024a,
title = {EWOk: Towards Efficient Multidimensional Compression of Indoor Positioning Datasets},
author = {Lucie Klus and Roman Klus and Joaquín Torres-Sospedra and Elena Simona Lohan and Carlos Granell-Canut and Jari Nurmi},
doi = {10.1109/TMC.2023.3277333},
issn = {1558-0660},
year = {2024},
date = {2024-03-01},
journal = {IEEE Transactions on Mobile Computing},
volume = {25},
number = {5},
pages = {3589-3604},
abstract = {Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance.},
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pubstate = {published},
tppubtype = {article}
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Indoor positioning performed directly at the end-user device ensures reliability in case the network connection fails but is limited by the size of the RSS radio map necessary to match the measured array to the device’s location. Reducing the size of the RSS database enables faster processing, and saves storage space and radio resources necessary for the database transfer, thus cutting implementation and operation costs, and increasing the quality of service. In this work, we propose EWOk, an Element-Wise cOmpression using k-means, which reduces the size of the individual radio measurements within the fingerprinting radio map while sustaining or boosting the dataset’s positioning capabilities. We show that the 7-bit representation of measurements is sufficient in positioning scenarios, and reducing the data size further using EWOk results in higher compression and faster data transfer and processing. To eliminate the inherent uncertainty of k-means we propose a data-dependent, non-random initiation scheme to ensure stability and limit variance. We further combine EWOk with principal component analysis to show its applicability in combination with other methods, and to demonstrate the efficiency of the resulting multidimensional compression. We evaluate EWOk on 25 RSS fingerprinting datasets and show that it positively impacts compression efficiency, and positioning performance. |
Bravenec, Tomás Exploiting Wireless Communications for Localization: Beyond Fingerprinting PhD Thesis Universitat Jaume I. INIT, 2023. Abstract | Links | BibTeX @phdthesis{Bravenec2023d,
title = {Exploiting Wireless Communications for Localization: Beyond Fingerprinting},
author = {Tomás Bravenec},
url = {http://hdl.handle.net/10803/689593},
doi = {http://dx.doi.org/10.6035/14124.2023.868082},
year = {2023},
date = {2023-12-18},
school = {Universitat Jaume I. INIT},
abstract = {The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field.},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
The field of Location-based Services (LBS) has experienced significant growth over the past decade, driven by increasing interest in fitness tracking, robotics, and eHealth. This dissertation focuses on evaluating privacy measures in Indoor Positioning Systems (IPS), particularly in the context of ubiquitous Wi-Fi networks. It addresses non-cooperative user tracking through the exploitation of unencrypted Wi-Fi management frames, which contain enough information for device fingerprinting despite MAC address randomization. The research also explores an algorithm to estimate room occupancy based on passive Wi-Fi frame sniffing and Received Signal Strength Indicator (RSSI) measurements. Such room occupancy detection has implications for energy regulations in smart buildings. Furthermore, the thesis investigates methods to reduce computational requirements of machine learning and positioning algorithms through optimizing neural networks and employing interpolation techniques for IPS based on RSSI fingerprinting. The work contributes datasets, analysis scripts, and firmware to improve reproducibility and supports advancements in the LBS field. |
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests Journal Article IEEE Journal of Indoor and Seamless Positioning and Navigation, 1 , pp. 221-230, 2023, ISSN: 2832-7322. Abstract | Links | BibTeX @article{Bravenec2023c,
title = {UJI Probes Revisited: Deeper Dive Into the Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/JISPIN.2023.3335882},
issn = {2832-7322},
year = {2023},
date = {2023-11-22},
journal = {IEEE Journal of Indoor and Seamless Positioning and Navigation},
volume = {1},
pages = {221-230},
abstract = {This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article centers on the deeper presentation of a new and publicly accessible dataset comprising Wi-Fi probe requests. Probe requests fall within the category of management frames utilized by the 802.11 (Wi-Fi) protocol. Given the ever-evolving technological landscape and the imperative need for up-to-date data, research on probe requests remains essential. In this context, we present a comprehensive dataset encompassing a one-month probe request capture conducted in a university office environment. This dataset accounts for a diverse range of scenarios, including workdays, weekends, and holidays, accumulating over 1 400 000 probe requests. Our contribution encompasses a detailed exposition of the dataset, delving into its critical facets. In addition to the raw packet capture, we furnish a detailed floor plan of the office environment, commonly referred to as a radio map, to equip dataset users with comprehensive environmental information. To safeguard user privacy, all individual user information within the dataset has been anonymized. This anonymization process rigorously balances the preservation of users' privacy with the dataset's analytical utility, rendering it nearly as informative as raw data for research purposes. Furthermore, we demonstrate a range of potential applications for this dataset, including but not limited to presence detection, expanded assessment of temporal received signal strength indicator stability, and evaluation of privacy protection measures. Apart from these, we also include temporal analysis of probe request transmission frequency and period between Wi-Fi scans as well as a peak into possibilities with pattern analysis. |
Bravenec, Tomás; Torres-Sospedra, Joaquín; Gould, Michael; Fryza, Tomas UJI Probes: Dataset of Wi-Fi Probe Requests Inproceedings 2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-6, IEEE, 2023, ISBN: 979-8-3503-2012-1. Abstract | Links | BibTeX @inproceedings{Bravenec2023b,
title = {UJI Probes: Dataset of Wi-Fi Probe Requests},
author = {Tomás Bravenec and Joaquín Torres-Sospedra and Michael Gould and Tomas Fryza},
doi = {https://doi.org/10.1109/IPIN57070.2023.10332508},
isbn = {979-8-3503-2012-1},
year = {2023},
date = {2023-09-25},
booktitle = {2023 13th International Conference on Indoor Positioning and Indoor Navigation (IPIN)},
pages = {1-6},
publisher = {IEEE},
abstract = {This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper focuses on the creation of a new, publicly available Wi-Fi probe request dataset. Probe requests belong to the family of management frames used by the 802.11 (Wi-Fi) protocol. As the situation changes year by year, and technology improves probe request studies are necessary to be done on upto-date data. We provide a month-long probe request capture in an office environment, including work days, weekends, and holidays consisting of over 1 400 000 probe requests. We provide a description of all the important aspects of the dataset. Apart from the raw packet capture we also provide a Radio Map (RM) of the office to ensure the users of the dataset have all the possible information about the environment. To protect privacy, user information in the dataset is anonymized. This anonymization is done in a way that protects the privacy of users while preserving the ability to analyze the dataset to almost the same level as raw data. Furthermore, we showcase several possible use cases for the dataset, like presence detection, temporal Received Signal Strength Indicator (RSSI) stability, and privacy protection evaluation. |
Bravenec, Tomás; Gould, Michael; Fryza, Tomas; Torres-Sospedra, Joaquín Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms Journal Article IEEE Sensors Journal, 17 , pp. 20044-20054, 2023, ISSN: 1530-437X. Abstract | Links | BibTeX @article{Bravenec2023e,
title = {Influence of Measured Radio Map Interpolation on Indoor Positioning Algorithms},
author = {Tomás Bravenec and Michael Gould and Tomas Fryza and Joaquín Torres-Sospedra},
doi = {https://doi.org/10.1109/JSEN.2023.3296752},
issn = {1530-437X},
year = {2023},
date = {2023-08-01},
journal = {IEEE Sensors Journal},
volume = {17},
pages = {20044-20054},
abstract = {Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Indoor positioning and navigation increasingly have become popular, and there are many different approaches, using different technologies. In nearly all of the approaches, the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation radio map (RM) by analyzing the environment. As this is usually done on a regular grid, the collection of received signal strength indicator (RSSI) data at every reference point (RP) of an RM is a time-consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful, as it allows researchers to spend more time with research instead of data collection. In this article, we analyze the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using five ESP32 microcontrollers working in monitoring mode and placed around our office. We then analyze the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian process regression (GPR) to find balance among final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements. |
