Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map

Dinh, Thai-Mai Thi; Duong, Ngoc-Son; Sandrasegaran, Kumbesan

doi:10.1109/jsen.2020.2989411

Cited by 112 publications

(60 citation statements)

References 21 publications

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“…In practice, this assumption is impractical because the user often does not aware his location in the building. Only few works set this point as (i) the first Wi-Fi AP that the smartphone connects with [18], [19], (ii) the strongest iBeacon that the smartphones captures on its scanning list [24], [28], or (iii) a fusion location by weighting factors between (i) and (ii) [21]- [23].…”

Section: ) Smartphone Orientationmentioning

confidence: 99%

“…Several works [21]- [24] adopted fusion algorithms that combine PDR and Wi-Fi/iBeacon to achieve consistently precise localization. In general, given an indoor map which consists of Wi-Fi AP/iBeacon locations and their corresponding offline training database, the key ideas of the fusion algorithms can be summarized as follows.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Unlike GPS, Wi-Fi or conventional Bluetooth, iBeacon offers high-precision location information and saves much more energy. Recent works [21]- [24] demonstrate that the hybrid PDR and Wi-Fi/PDR approaches can benefit from user mobility information and additional measurements from nearby Wi-Fi APs and iBeacons. Specially, Wi-Fi/iBeacon measurements collected by the smartphones can be served as reference data.…”

Section: Introductionmentioning

confidence: 99%

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A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Nguyen

Shin

2021

IEEE Access

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Considering smartphone-based indoor localization and tracking, combination of Wi-Fi fingerprinting and pedestrian dead reckoning (PDR) becomes prevalent due to wide deployment of indoor Wi-Fi access points and easily access of inertial measurement units (IMUs) on smartphones. Since Wi-Fi fingerprinting depends on the similarities between online and offline received signal strengths, it suffers from the fluctuation of radio signal measurements. Meanwhile, the PDR relies on the IMUs, which undergo accumulated errors in long walking distance. In this paper, we aim to improve the indoor tracking accuracy, while filling up the gap between two popular Android and iOS platforms. In particular, we propose a hybrid tracking algorithm that integrates the PDR with Wi-Fi/iBeacon signal features. There are three key points in our paper. First, we process Wi-Fi/iBeacon signals to build conversion functions to convert signals to related proximity distances. Besides, in order to support mobile tracking, we introduce a placement methodology for iBeacon installation for a specific region of interest. Second, a mobile smartphone uses an improved PDR to localize itself, while incorporating Wi-Fi/iBeacon data for estimating a starting point and correcting its location along the walking path. Finally, we build an iOS app to implement the proposed scheme and display visual tracking results. Experiment results show that our proposed scheme is more robust and accurate compared to the conventional schemes.INDEX TERMS smartphone based indoor localization; pedestrian dead reckoning; iBeacon placement methodology; Wi-Fi proximity

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Section: ) Smartphone Orientationmentioning

confidence: 99%

Section: Related Work and Motivationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Nguyen

Shin

2021

IEEE Access

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“…sends a notification or a discount coupon) [65]. Fingerprint approaches based on iBeacon have been proposed in the literature [66] [67].…”

Section: Bluetoothmentioning

confidence: 99%

Room-Level Localization System Based on LoRa Backscatters

2021

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The aim of this paper is to propose a novel room-level localization approach to locate LoRa backscatter devices, which can be easily embedded into wearable devices or smartphones. The advantages of this system lie in its series of low-cost, low-power, low-complexity and long-range features. LoRa backscattering operates by alternatively connecting an antenna, through a switch, to two loads with high reflection coefficients and opposite phase. The result is a frequency shift of the LoRa incident signal equal to the backscatter switching frequency. The localization system comprises several LoRa receivers distributed among the rooms, a LoRa transmitter located at a central point and the backscatter device, which is carried or worn by a subject. The position of the LoRa backscatter device can be determined by comparing the received signal strength between all receivers. In order to improve the accuracy of the system, different machine learning classifiers were compared. System performance was tested in a real-life scenario, achieving an accuracy up to 89.7% using linear discriminant analysis (LDA). INDEX TERMS Backscatter communications, LoRa, localization, wireless sensor networks, RFID, zeropower sensor, internet of things (IoT).

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“…The fingerprinting method compares a received signal strength list measured at the positioning time with a signal map constructed during the training phase to determine the position of a device on the signal map. Among the devices that can be applied to the fingerprinting method are Wi-Fi [1,2], Bluetooth [3,4], and magnetometer sensors [5]. In particular, the fingerprinting method is widely used in commercial Wi-Fi positioning systems, and many methods for overcoming disadvantages such as an increase in collection costs are being actively studied [6].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations

Lee

Kim

2020

Applied Sciences

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The use of smartphones for accurate navigation in underground spaces, such as subway stations, poses several challenges. This is because it is difficult to obtain a sure estimate of user location due to the radio signal interference caused by the entry and exit of trains, the infrastructure of the subway station installation, and changes in the internal facility environment. This study uses quick response markers and augmented reality to solve these difficulties using an error correction method. Specifically, a hybrid marker-based indoor positioning system (HMIPS) which provides accurate and efficient user-tracking results is proposed. The HMIPS performs hybrid localization by using marker images as well as inertial measurement unit data from smartphones. It utilizes the Viterbi tracking algorithm to solve the problem of tracking accuracy degradation that may occur when inertial sensors are used by adopting a sensor error correction technique. In addition, as an integrated system, the HMIPS provides a tool to easily carry out all the steps necessary for positioning. The results of experiments conducted in a subway station environment confirm that the HMIPS provides accurate and practical navigation services. The proposed system is expected to be useful for indoor navigation, even in poor indoor positioning environments.

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Smartphone-Based Indoor Positioning Using BLE iBeacon and Reliable Lightweight Fingerprint Map

Cited by 112 publications

References 21 publications

A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

A Precise Tracking Algorithm Using PDR and Wi-Fi/iBeacon Corrections for Smartphones

Room-Level Localization System Based on LoRa Backscatters

A Hybrid Marker-Based Indoor Positioning System for Pedestrian Tracking in Subway Stations

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