iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

Wu, Xudong; Shen, Ruofei; Fu, Luoyi; Tian, Xiaohua; Liu, Peng; Wang, Xinbing

doi:10.1109/access.2017.2726088

Cited by 53 publications

(27 citation statements)

References 24 publications

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“…Figure 16a. The pedestrian started from the position of A(18,0), walked forward to B(0,15) and C(0,−5), left laterally walked to D(8,−5), walked backward to E(8,0), and then turned and right laterally walked to A (18,0). This process was continuously repeated 10 times, and the point E was selected as the reference point.…”

Section: Experiments Results Of Drwms Algorithmmentioning

confidence: 99%

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Chen

et al. 2019

Remote Sensing

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More and more applications of location-based services lead to the development of indoor positioning technology. Wi-Fi-based indoor localization has been attractive due to its extensive distribution and low cost properties. IEEE 802.11-2016 now includes a Wi-Fi Fine Time Measurement (FTM) protocol which provides a more robust approach for Wi-Fi ranging between the mobile terminal and Wi-Fi access point (AP). To improve the positioning accuracy, in this paper, we propose a robust dead reckoning algorithm combining the results of Wi-Fi FTM and multiple sensors (DRWMs). A real-time Wi-Fi ranging model is built which can effectively reduce the Wi-Fi ranging errors, and then a multisensor multi-pattern-based dead reckoning is presented. In addition, the Unscented Kalman filter (UKF) is applied to fuse the results of Wi-Fi ranging model and multiple sensors. The experiment results show that the proposed DRWMs algorithm can achieve accurate localization performance in line-of-sight/non-line-of-sight (LOS)/(NLOS) mixed indoor environment. Compared with the traditional Wi-Fi positioning method and the traditional dead reckoning method, the proposed algorithm is more stable and has better real-time performance for indoor positioning.

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Section: Experiments Results Of Drwms Algorithmmentioning

confidence: 99%

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Chen

et al. 2019

Remote Sensing

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“…The main shortcoming of these works is the lack of studying the effect of interference, environmental characteristics and multipath. A recent study [26] on modeling BLE's ADV_CHs for indoor positioning carried out a series of experiments in a library using iBeacon. Although the authors have used LNSM, they did not report the parameters of this model.…”

Section: Related Workmentioning

confidence: 99%

Empirical analysis and modeling of Bluetooth low-energy (BLE) advertisement channels

Nikoukar

Abboud

Samadi

et al. 2018

2018 17th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net)

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Bluetooth Low Energy (BLE) is a widely-used lowpower wireless standard in the Internet of Things (IoT) domain. This standard provides a set of advertisement channels, which are primarily used for device discovery, connection initiation, and information broadcast. Beacon transmission over these advertisement channels is the enabler of applications such as indoor positioning, product advertisement, and medical monitoring. Meanwhile, the performance and accuracy of these applications highly depend on the characteristics of communication over advertisement channels. Unfortunately, the existing literature does not offer an extensive characterization of these channels under various operational conditions. In this paper, we address this research gap through conducting extensive experiments in four different environments. We study the effect of environment and interference on noise floor and signal propagation, and we present a model for noise floor and extract the parameters of lognormal path loss model. The proposed models, in particular, can be directly used in simulation tools for modeling BLE wireless channels as well as applications such as indoor positioning. Index Terms-Bluetooth Low Energy (BLE), Log-Normal Shadowing Model (LNSM), multi-path fading, Bluetooth beacon, channel modeling, simulation.

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“…The ranging error showed about 2-m accuracy. Xudong et al [33] proposed a Bluetooth-based indoor localization approach that jointly uses iBeacon [34] and inertial sensors in large open areas. Their performance showed 90% errors within 3.5-m ranging accuracy.…”

Section: Localization and Tracking Technologymentioning

confidence: 99%

Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications

et al. 2018

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Recently, the number of tunnels is increasing due to urbanization, and fire accidents in tunnels are likewise increasing. In particular, in a long tunnel of more than 1 km it is very difficult to track the exact location of a fire, accident vehicles, and the fire brigade, as well as whether a fire occurred. In this paper, we analyze various types of accidents that may occur in tunnel fires and propose detection, search, and rescue techniques to cope with them. For early detection of accidents, we propose various sensors using Internet of Things (IoT) technology and sensor networks to connect them. These sensors can detect not only a fire but also the position of the vehicle in which the fire is occurring in real time. We also propose a robotic system and operation technique that can be controlled by a fire fighter for more precise search operation. For rescue procedures, localization and tracking technology for fire fighters and robots is proposed. Finally, the efficiency of the proposed system was verified through actual performance tests, including simulations of actual placement and operation in tunnels. Through the construction of the equipment in an actual tunnel 1.9 km long, we show that the proposed system is good enough to cope with fire accidents, in terms of the delivery ratio of the collected data, fire recognition ratio, localization accuracy, and response delay.

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iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

Cited by 53 publications

References 24 publications

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

A Robust Dead Reckoning Algorithm Based on Wi-Fi FTM and Multiple Sensors

Empirical analysis and modeling of Bluetooth low-energy (BLE) advertisement channels

Smart Disaster Response in Vehicular Tunnels: Technologies for Search and Rescue Applications

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