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.
Citations
More filters
TL;DR: In FastGraph Enhanced, the 3D Force-Directed Graph-Based method, used to model the radio environment, is extended with new algorithms, allowing to improve, among other aspects, the positioning performance.
Abstract: FastGraph is a novel positioning approach recently proposed to address the challenges of positioning in large spaces. Wi-Fi-based indoor positioning solutions often require complex and time-consuming deployments. Fingerprinting, as one of most used approaches, relies on a radio map, usually created by manual site survey, a process unpractical even for small spaces. Moreover, the site survey has to be repeated frequently due to the changes in the radio environment. Wi-Fi-based solutions are also frequently discarded for applications such as indoor vehicle navigation due to limited accuracy. This article introduces FastGraph Enhanced, a new version of FastGraph, able to provide high accuracy positioning, opening new fields of application, such as navigation for autonomous vehicles. In FastGraph Enhanced, the 3D Force-Directed Graph-Based method, used to model the radio environment, is extended with new algorithms, allowing to improve, among other aspects, the positioning performance. The core advantages of FastGraph are maintained, not requiring previous calibration or knowledge about the space. The proposed solution was evaluated in real world, with very significative improvements in positioning accuracy when compared with the basic version of FastGraph (from around 5m to 0.5m), and with state-of-the-art solutions.
6 citations
Cites background from "Location Fingerprinting With Blueto..."
...This was also verified in [53], where the authors compare the scan times of Wi-Fi and Bluetooth....
[...]
TL;DR: In this article , a comprehensive survey of Bluetooth localization is presented, including the measurements for localization, working principles, and method comparison, highlighting the learning-based methods and integrated localization methods.
Abstract: The rapid development of the Bluetooth technology offers a possible solution for indoor localization scenarios. Compared with other indoor localization technologies, such as vision, light detection and ranging, ultrawide band, etc., Bluetooth has been characterized by low cost, easy deployment, low energy consumption, and potentially high localization accuracy, which enable itself to be a competitive technology in indoor location-based services, the Internet of Things, and many other fields. In this article, we first present a comprehensive survey of Bluetooth localization technology, including the measurements for localization, working principles, and method comparison. We highlight the learning-based methods and integrated localization methods. Then, we review the applications and existing commercial solutions, revealing the possible directions for the industrialization of Bluetooth localization. Finally, this article proposes several open issues of Bluetooth localization (e.g., multichannel difference, multipath, co-channel interference, and device heterogeneity) and projects several future trends.
6 citations
01 Sep 2019
TL;DR: An algorithm for estimating the misalignment between the pedestrian and the device by evaluating the acceleration data fit a simplified gait model in each direction is proposed.
Abstract: In recent times, infrastructure-free indoor positioning has been an important topic of research. Many of the proposed systems are based on pedestrian dead reckoning, thus relying on estimating the heading of the pedestrian. While many studies successfully address the problem of estimating the heading of the device, current approaches have the limitation of requiring the device to be aligned with the pedestrian. To address this problem, we propose an algorithm for estimating the misalignment between the pedestrian and the device by evaluating the acceleration data fit a simplified gait model in each direction. Contrary to similar algorithms, the proposal in this paper does not require a previously trained model nor the detection of steps, and can be implemented using only acceleration data. Furthermore, our experimental results show a significant improvement over the current state of the art.
6 citations
Cites background from "Location Fingerprinting With Blueto..."
...While there are systems that report sub-metric precision, these rely on specialized infrastructure [3]–[5], elevating setup and maintenance costs and thus limiting their deployability....
[...]
01 Sep 2019
TL;DR: An indoor location system with high precision and continuous position monitoring in real time with the use of a mobile phone without any special hardware using only commercially available low-cost sensors and can predict the correct user location in 72% of the cases with accuracy less than 1 meter.
Abstract: The objective of this work is to develop an indoor location system with high precision and continuous position monitoring in real time with the use of a mobile phone without any special hardware using only commercially available low-cost sensors. Finding the location is done using the measured Received Signal Strength Indicator (RSSI) value of Bluetooth beacons received from mobile phones combined with measurements from other phone sensors. For the development of our model, we collected measurements for the RSSI values from Beacons which we placed in a space of 30.75 sqm and the values from the mobile accelerometer in motion. We divided the space into 16 subareas of 1.45m x 1.35m and used our measurements to develop a machine learning model using the open source TensorFlow framework to predict the correct subarea of the user. Through experiments, we show that our model can reach an accuracy of 0.7209 which means that our system can predict the correct user location in 72% of the cases with accuracy less than 1 meter.
6 citations
Cites background from "Location Fingerprinting With Blueto..."
...Bluetooth beacons are more easily deployed (especially if battery powered) and not constrained by the need to provide uniform communications coverage [3], [5], [17], [18] ....
[...]
...Indoor localization approaches today based on RSSI of wireless signals such as WiFi and Bluetooth are the most popular [1], [2], [3] due to their low infrastructure cost, their use in all smartphones and potential high accuracy....
[...]
...Most approaches are based (at least) on the measurement of the RSSI value from wireless signals such as WiFi and Bluetooth [2] , [3]....
[...]
01 Sep 2019
TL;DR: This paper proposes a system, InFo, that can leverage real-time visual information captured by surveillance cameras and augment that with images captured by the smart device user to deliver accurate discretized location information.
Abstract: Localization in an indoor or Global Positioning System (GPS)-denied environment is paramount. It drives various applications that require locating humans or robots in an unknown environment. Various localization systems using different ubiquitous sensors such as camera, radio frequency, inertial measurement unit have been developed. Most of these systems cannot accommodate for scenarios which have substantial changes in the environment such as a large number of people (unpredictable) and sudden change in the environment floor plan (unstructured). In this paper, we propose a system, InFo that can leverage real-time visual information captured by surveillance cameras and augment that with images captured by the smart device user to deliver accurate discretized location information. Through our experiments, we demonstrate that our deep learning based InFo system provides an improvement of 10% as compared to a system that does not utilize this real-time information.
6 citations
Cites methods from "Location Fingerprinting With Blueto..."
...Numerious localization and tracking systems have been developed using different IoT sensors such as BLE [15], Wi-Fi [5], [11] and Inertial Measurement Unit (IMU) [22]....
[...]
...Similarly, Faragher and Harle [11] used a fingerprinting technique to analyze the performance of BLE based localization using K-NNs and proximity-based techniques....
[...]
...In this technique a fingerprint map of the environment for the RSSI signal is generated offline, which is then used for real-time signals [11], [38]....
[...]
References
More filters
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]....
[...]
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....
[...]
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]....
[...]
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]....
[...]