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Journal ArticleDOI

Location Fingerprinting With Bluetooth Low Energy Beacons

06 May 2015-IEEE Journal on Selected Areas in Communications (IEEE)-Vol. 33, Iss: 11, pp 2418-2428
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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Citations
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Journal ArticleDOI
11 Dec 2018-Energies
TL;DR: This study explored Bluetooth Low Energy (BLE) Beacon indoor positioning for smart home power management and proposed a novel system framework to detect the user location, and to perform power management in the home through a mobile device application.
Abstract: In recent years, smart homes have begun to use various sensors to detect the location of users indoors. However, such sensors may not be stable, resulting in high detection error rates. Thus, how to improve indoor positioning accuracy has become an important topic. This study explored Bluetooth Low Energy (BLE) Beacon indoor positioning for smart home power management. A novel system framework using BLE Beacon was proposed to detect the user location, and to perform power management in the home through a mobile device application. Since the BLE Beacon may produce a multipath effect, this study used the positioning algorithm and hardware configuration to reduce the error rate. Location fingerprint positioning algorithm and filter modification were used to establish a positioning method for facilitating deployment, and to reduce the required computing resources. The experiments included an observation of the Received Signal Strength Indicators (RSSI) and selecting filters and a discussion of the relationship between the characteristics of the BLE Beacon signal accuracy and the number of the BLE Beacons deployed in the observation space. The BLE Beacon multilateration positioning was combined with the In-Snergy intelligent energy management system for smart home power management. The contribution of this study is to allow users to enjoy smart home services based on their location within the home using a mobile device application.

24 citations

Journal ArticleDOI
TL;DR: A BLE-based tracking system to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis is proposed, and a new particle Markov chain model is proposed to evaluate the LA-level performance regarding the visibility area in an environment with large obstacles.
Abstract: This paper describes research toward a tracking system for locating persons indoor based on low-cost Bluetooth Low Energy (BLE) beacons. Wireless fingerprinting based on BLE beacons has emerged as an increasingly popular solution for fine-grained indoor localization. Inspired by the idea of mobility tracking used in the cellular network, this paper proposes a BLE-based tracking system, designated as BTrack, to learn the location area (LA) of an indoor user based on the reported wireless fingerprinting combined with statistical analysis. We propose a new particle Markov chain model to evaluate the LA-level performance regarding the visibility area in an environment with large obstacles. In the presence of sight obstructions, BTrack is evaluated using a real-world test bed built in a library with tall bookshelves. The performance of the proposed system is evaluated in terms of the mean distance error and the LA prediction accuracy considering the direct line-of-sight. Compared with the existing methods, BTrack reduces the average localization error by 25% and improves the average prediction accuracy by more than 16% given a random mobility pattern.

23 citations


Cites methods from "Location Fingerprinting With Blueto..."

  • ...Therefore, the use of BLE beacons is an alternative for ILBS [21]....

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  • ...Therefore, high localization accuracy (within meter range) is still expected in order to offer satisfactory ILBS....

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  • ...I. INTRODUCTION Indoor Location-based Services (ILBS) have attracted much attention in recent years due to the growing commercial demand [1], [2]....

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  • ...The BTrack system learns the user location (i.e., the cell and the LA) and acts as a mediator that provides user location to other advanced ILBS....

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  • ...The ILBS app is implemented on top of the TCP/IP protocol between the handheld/wearable device and the server....

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Journal ArticleDOI
TL;DR: INOA is proposed, a scalable and practical locality classification overcoming the above challenges, and may serve as a plug-in before any fingerprint-based localization, and can be incrementally extended to cover new areas or regions for large-scale deployment.
Abstract: Locality classification is an important component to enable location-based services. It entails two sequential queries: 1) whether a target is within the site or not, i.e., inside/outside region decision, and 2) if so, which area in the region the target is located, i.e., area classification. Locality classification is hence more coarse-grained and efficient as compared with pinpointing the exact target location in the region. The classification problem is challenging, because fingerprints may not exist outside the region for training. Furthermore, the target may sample an incomplete RSSI vector due to, say, random signal noise, momentary occlusion, or scanning duration. The algorithm also has to be computationally efficient. We propose INOA, a scalable and practical locality classification overcoming the above challenges. INOA may serve as a plug-in before any fingerprint-based localization, and can be incrementally extended to cover new areas or regions for large-scale deployment. Its preprocessor cherry-picks only those discriminating access points, which greatly enhances computational efficiency and accuracy. By formulating a “one-class” classifier using ensemble learning, INOA accurately decides whether the target is within the region or not. Extensive experimental trials in different sites validate the high efficiency and accuracy of INOA, without the need of full RSSI vectors collected at the target.

23 citations


Cites background from "Location Fingerprinting With Blueto..."

  • ...or emerging fingerprint signals such as Bluetooth [7] or...

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  • ...For concreteness, we consider Wi-Fi RSSIs in our expositions and experiments, though INOA is applicable to any existing or emerging fingerprint signals such as Bluetooth [7] or channel state information [8]....

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Journal ArticleDOI
TL;DR: This article proposes a self-calibration time-reversal (TR) fingerprinting localization approach to mitigate the effects of environmental changes without updating the fingerprint database.
Abstract: With the increasing demands for indoor location-based services, fingerprinting-based localization attracts considerable attention, due to that it could achieve high localization accuracy using simple equipment. However, the main problem of fingerprinting localization is that with the change of the indoor environment, the fingerprint database would be outdated, which inevitably leads to localization performance degradation. To tackle this issue, based on deep learning, this article proposes a self-calibration time-reversal (TR) fingerprinting localization approach to mitigate the effects of environmental changes without updating the fingerprint database. In the offline stage, the amplitude autoencoder (A-AE) and the phase autoencoder (P-AE) are, respectively, trained without labels to record features of the current environment. In the online stage, the trained A-AE and P-AE are used to adaptively calibrate the real-time measurements which may have been distorted due to the environmental changes. Based on the calibrated measurements, a modified TR resonating strength (TRRS) is presented for localization. The experimental results confirm the effectiveness of the proposal.

23 citations


Cites background from "Location Fingerprinting With Blueto..."

  • ...Nevertheless, some common indoor localization methods based on wireless fidelity (Wi-Fi) [1]–[5], Bluetooth [6], [7], infrared [8], visible light [9], radio-...

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Book ChapterDOI
01 Jan 2021
TL;DR: This chapter focuses on indoor positioning technologies with smartphones, and in particular, emphasize the technologies based on radio frequency (RF) and built-in sensors.
Abstract: Global Navigation Satellite Systems (GNSS) have achieved great success in providing localization information in outdoor open areas. However, due to the weakness of the signal, GNSS signals cannot be received well indoors. Currently, indoor positioning plays a significant role in many areas, such as the Internet of Things (IoT) and artificial intelligence (AI), but given the complexity of indoor spaces and topology, it is still challenging to achieve an accurate, effective, full coverage and real-time positioning solution indoors. With the development of information technology, the smartphone has become more and more popular. With a large number of sensors embedded in smartphones, it is thus possible to achieve low cost, continuity, and high usability for indoor positioning. In this chapter, we focus on indoor positioning technologies with smartphones, and in particular, emphasize the technologies based on radio frequency (RF) and built-in sensors. The pros and cons of the technologies are reviewed and discussed in the context of different applications. Moreover, the challenges of indoor positioning are pointed out and the directions for the future development of this area are discussed.

22 citations

References
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Journal ArticleDOI
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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Proceedings ArticleDOI
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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Journal ArticleDOI
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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