Location Fingerprinting With Bluetooth Low Energy Beacons
TL;DR: This work provides a detailed study of BLE fingerprinting using 19 beacons distributed around a ~600 m2 testbed to position a consumer device, and investigates the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency.
Abstract: The complexity of indoor radio propagation has resulted in location-awareness being derived from empirical fingerprinting techniques, where positioning is performed via a previously-constructed radio map, usually of WiFi signals. The recent introduction of the Bluetooth Low Energy (BLE) radio protocol provides new opportunities for indoor location. It supports portable battery-powered beacons that can be easily distributed at low cost, giving it distinct advantages over WiFi. However, its differing use of the radio band brings new challenges too. In this work, we provide a detailed study of BLE fingerprinting using 19 beacons distributed around a $\sim\! 600\ \mbox{m}^2$ testbed to position a consumer device. We demonstrate the high susceptibility of BLE to fast fading, show how to mitigate this, and quantify the true power cost of continuous BLE scanning. We further investigate the choice of key parameters in a BLE positioning system, including beacon density, transmit power, and transmit frequency. We also provide quantitative comparison with WiFi fingerprinting. Our results show advantages to the use of BLE beacons for positioning. For one-shot (push-to-fix) positioning we achieve $30\ \mbox{m}^2$ ), compared to $100\ \mbox{m}^2$ ) and < 8.5 m for an established WiFi network in the same area.
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TL;DR: The experimental results indicate the substantial performance gain of the proposed DCAE in comparison with state-of-the-art autoencoders, and the classifier trained using the fingerprints learned by the DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5.
Abstract: Device-free occupancy detection is very important for certain Internet of Things applications that do not require the user to carry a receiver. This paper achieves the device-free occupancy detection with RF fingerprinting, which labels each zone with a $2M$ -dimensional fingerprint vector. Specifically, the fingerprint vector consists of received signal strength (RSS) values measured from $M$ Bluetooth low energy (BLE) beacons and also their corresponding temporal RSS variations. However, the unreliable RSS values caused two common issues with the fingerprint vector: 1) noise and 2) sparsity. To this end, we propose denoising-contractive autoencoder (DCAE) to jointly deal with these two issues, by learning a robust fingerprint prior to device-free occupancy detection. We validate the performance of our proposed DCAE with large-scale real-world datasets. The experimental results indicate the substantial performance gain of our proposed DCAE in comparison with state-of-the-art autoencoders. In particular, the classifier trained using the fingerprints learned by our proposed DCAE is able to maintain at least 90% accuracy when the noise factor or sparsity ratio increases to 0.6 and 0.5, respectively.
10 citations
Cites background or methods from "Location Fingerprinting With Blueto..."
...On the other hand, Faragher and Harle [10] constructed the RF fingerprint with the RSS values from the deployed beacons....
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...[9] and Faragher and Harle [10] assumed that the users always carry with them a smartphone....
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...Previous work [10] has shown that BLE beacons are the potential infrastructure for RF fingerprint in comparison to the WiFi access points....
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...1) Device-Based Occupancy Detection: There have been many research works on detecting the presence/position/location of a mobile device [9], [10] using wireless signals from BLE beacons....
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...Many existing works simply use the raw RSS measurements to construct the fingerprint vector [10], [29], [30]....
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TL;DR: This paper introduces a new concept of how individual consumers can follow their own understanding of sustainability, while at the same time benefiting from collective and participatory actions.
Abstract: We are living in a world of vast information. The means of the Internet allow access to diverse sources of information, with social media and Internet of Things technologies significantly expanding the informational ecosystem. With the use of social media, it is easy for ‘like-minded' people to group up and initiate movements. One way to articulate such movements is via political consumerism. Users group together and boycott or buycott (boost purchases) for certain products with a concrete collective goal in mind. If, however, the collective goal is vague and abstract, as in the case of sustainability, this bottom-up strategy may lose its popularity and attraction. In this paper, we introduce a new concept of how individual consumers can follow their own understanding of sustainability, while at the same time benefiting from collective and participatory actions. We discuss how the means of ICT can be used to develop political consumerism further to transform individual policies into collective statements.
10 citations
Cites methods from "Location Fingerprinting With Blueto..."
..., the earth’s magnetic field as described in [Haverinen and Kemppainen, 2009] and [Haverinen, 2016], an artificial magnetic field, or the RSSI values of WLAN/BLE beacons as performed in [Faragher and Harle, 2015]....
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...…of fingerprinting rely on tracking algorithms with knowing the distribution of, e.g., the earth’s magnetic field as described in (Haverinen and Kemppainen, 2009) and (Haverinen, 2016), an artificial magnetic field, or the RSSI values of WLAN/BLE beacons as performed in (Faragher and Harle, 2015)....
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08 Jan 2019
TL;DR: This work studied people’s understandings of how beacon-based systems work and identified several factors that shaped their understandings or misunderstandings, such as how information flows among the components of beacon systems and who owns the beacons.
Abstract: Bluetooth beacon technology is an emerging location-based Internet of Things (IoT) technology, designed to transform proximity-based services in various domains such as retail. Beacons are part of the IoT infrastructure, but people rarely interact with them directly and yet they could still pose privacy risks to users. However, little is known about people’s understandings of how beacon-based systems work. This is an important question since it can influence people’s perceptions, adoption, and usage of this emerging technology. Drawing from 22 semi-structured interviews, we studied people’s understandings of how beacon-based systems work and identified several factors that shaped their understandings or misunderstandings, such as how information flows among the components of beacon systems and who owns the beacons. These understandings and misunderstandings can potentially pose significant privacy risks to beacon users.
10 citations
Cites background from "Location Fingerprinting With Blueto..."
...This implies that our participants either ignored or did not know that Bluetooth could also be used for location tracking [9, 10]....
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...However, beacon-based systems could still pose privacy risks to their users because these systems have the ability to track people’s location through Bluetooth [9, 10]....
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10 citations
14 Apr 2017
TL;DR: A Support Vector Machine (SVM) classification algorithm based crowdsourcing method is developed and applied to generate BLE landmarks instead of manual work leveraging smartphone sensors and uploaded BLE signals for better localization performance and can be a promising methodology in practical usages.
Abstract: Indoor localization becomes a research focus in recent years since. Smartphone-based pedestrian dead reckoning (PDR) is one of the widely-adopted localization techniques with limiting problems such as the drift of inertial sensors. Bluetooth Low Energy (BLE) has better performance result which makes it an auxiliary tool for PDR to correct errors. But BLE fingerprint sampling and calibrating are time-consuming and labor-intensive. In this paper, a Support Vector Machine (SVM) classification algorithm based crowdsourcing method is developed and applied to generate BLE landmarks instead of manual work leveraging smartphone sensors and uploaded BLE signals. A particle filter is also used to fusion PDR and landmarks detection results for better localization performance. The experiments show that the proposed fusion algorithm achieved the accuracy of 3.15 m at 90% of the time with dense landmarks (1 landmark per 5 m), which performs 51.76% better than 6.53 m from PDR algorithm. With sparse landmarks (1 landmark per 15 m), the proposed fusion algorithm achieved the accuracy of 3.26 m at 90% of the time. The proposed tracking system using smartphone inertial sensors and BLE beacons can be a promising methodology in practical usages.
10 citations
Cites methods from "Location Fingerprinting With Blueto..."
...Comparing with fingerprint method, the localization accuracy of triangulation method is low which limits its application scenarios [9]....
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References
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01 Nov 2007
TL;DR: Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
Abstract: Wireless indoor positioning systems have become very popular in recent years. These systems have been successfully used in many applications such as asset tracking and inventory management. This paper provides an overview of the existing wireless indoor positioning solutions and attempts to classify different techniques and systems. Three typical location estimation schemes of triangulation, scene analysis, and proximity are analyzed. We also discuss location fingerprinting in detail since it is used in most current system or solutions. We then examine a set of properties by which location systems are evaluated, and apply this evaluation method to survey a number of existing systems. Comprehensive performance comparisons including accuracy, precision, complexity, scalability, robustness, and cost are presented.
4,123 citations
"Location Fingerprinting With Blueto..." refers background in this paper
...Indoor positioning is a mature research field, with many proposed technologies and techniques—comprehensive overviews can be found in [2], [18], [19]....
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06 Jun 2005
TL;DR: The Horus system identifies different causes for the wireless channel variations and addresses them and uses location-clustering techniques to reduce the computational requirements of the algorithm and the lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.
Abstract: We present the design and implementation of the Horus WLAN location determination system. The design of the Horus system aims at satisfying two goals: high accuracy and low computational requirements. The Horus system identifies different causes for the wireless channel variations and addresses them to achieve its high accuracy. It uses location-clustering techniques to reduce the computational requirements of the algorithm. The lightweight Horus algorithm helps in supporting a larger number of users by running the algorithm at the clients.We discuss the different components of the Horus system and its implementation under two different operating systems and evaluate the performance of the Horus system on two testbeds. Our results show that the Horus system achieves its goal. It has an error of less than 0.6 meter on the average and its computational requirements are more than an order of magnitude better than other WLAN location determination systems. Moreover, the techniques developed in the context of the Horus system are general and can be applied to other WLAN location determination systems to enhance their accuracy. We also report lessons learned from experimenting with the Horus system and provide directions for future work.
1,631 citations
"Location Fingerprinting With Blueto..." refers background in this paper
...Here the focus is on radio positioning, specifically using the empirical fingerprinting techniques [3], [15], [17], [22] that avoid the need to model the complex radio propagation environment indoors by patternmatching to a previously surveyed map of radio signal strengths....
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TL;DR: It is concluded that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours.
Abstract: With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.
749 citations
"Location Fingerprinting With Blueto..." refers background in this paper
...fingerprints with other sources to form hybrid systems, many of which are based on the idea of Simultaneous Localization and Mapping (SLAM) [10], [16] being applied to pedestrian dead reckoning [13]....
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01 Feb 2000
TL;DR: This paper analyzes shortcomings of the basic system, develops and evaluates solutions to address these shortcomings, and describes several new enhancements, including a novel access point-based environmental profiling scheme, and a Viterbi-like algorithm for continuous user tracking and disambiguation of candidate user locations.
Abstract: We address the problem of locating users inside buildings using a radio-frequency (RF) wireless LAN. A previous paper presented the basic design and a limited evaluation of a user-location system we have developed. In this paper, we analyze shortcomings of the basic system, and develop and evaluate solutions to address these shortcomings. Additionally, we describe several new enhancements, including a novel access point-based environmental profiling scheme, and a Viterbi-like algorithm for continuous user tracking and disambiguation of candidate user locations. Using extensive data collected from our deployment, we evaluate our system’s performance over multiple wireless LAN technologies and in different buildings on our campus. We also discuss significant practical issues that arise in implementing such a system. Our techniques are implemented purely in software and are easily deployable over a standard wireless LAN.
608 citations
01 Jun 2010
TL;DR: NTP version 4 (NTPv4), which is backwards compatible with NTP version 3 (N TPv3), described in RFC 1305, as well as previous versions of the protocol, are described.
Abstract: The Network Time Protocol (NTP) is widely used to synchronize computer
clocks in the Internet. This document describes NTP version 4 (NTPv4),
which is backwards compatible with NTP version 3 (NTPv3), described in
RFC 1305, as well as previous versions of the protocol. NTPv4 includes
a modified protocol header to accommodate the Internet Protocol
version 6 address family. NTPv4 includes fundamental improvements in
the mitigation and discipline algorithms that extend the potential
accuracy to the tens of microseconds with modern workstations and fast
LANs. It includes a dynamic server discovery scheme, so that in many
cases, specific server configuration is not required. It corrects
certain errors in the NTPv3 design and implementation and includes an
optional extension mechanism. [STANDARDS-TRACK]
605 citations
"Location Fingerprinting With Blueto..." refers methods in this paper
...Before each experiment, each clock was manually synchronized using a Network Time Protocol (NTP) server [20]....
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