Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi

Chen, Jing; Zhang, Yi; Xue, Wei

doi:10.3390/s18051378

Cited by 30 publications

(26 citation statements)

References 32 publications

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“…Another group of methods focuses on the automation of fingerprint generation [20] or crowdsourcing [8,21]. These methods aim to automate the collection and update of fingerprints (i.e., radio map) [22,23].…”

Section: Rss-based Methodsmentioning

confidence: 99%

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Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Liu

Yang

et al. 2020

Applied Sciences

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For localization in daily life, low-cost indoor positioning systems should provide real-time locations with a reasonable accuracy. Considering the flexibility of deployment and low price of iBeacon technique, we develop a real-time fusion workflow to improve localization accuracy of smartphone. First, we propose an iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations, and obtain accurate locations as the baseline result. Second, as turns are pivotal for positioning, we segment pedestrian trajectories according to turns. Then, we apply a Kalman filter (KF) to heading measurements in each segment, which improves the locations derived by pedestrian dead reckoning (PDR). Finally, we devise another KF to fuse the iBeacon-based approach with the PDR to overcome orientation noises. We implemented this fusion workflow in an Android smartphone and conducted real-time experiments in a building floor. Two different routes with sharp turns were selected. The positioning accuracy of the iBeacon-based method is RMSE 2.75 m. When the smartphone is held steadily, the fusion positioning tests result in RMSE of 2.39 and 2.22 m for the two routes. In addition, the other tests with orientation noises can still result in RMSE of 3.48 and 3.66 m. These results demonstrate our fusion workflow can improve the accuracy of iBeacon positioning and alleviate the influence of PDR drifting.

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Section: Rss-based Methodsmentioning

confidence: 99%

“…According to Kalman filtering, the update equations of this KF are presented in Equations 18- (20). The matrix H k in Equation 18is an identity matrix, and Z k refers to the BLE-derived locations at time slot k. The matrix R represents the covariance matrix of measurement noise (Equation 20).…”

Section: Fusion Methodsmentioning

confidence: 99%

Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Liu

Yang

et al. 2020

Applied Sciences

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“…However, it requires installation of network infrastructure and access points. In [111], smartphone sensors and Wi-Fi signal have been utilized to construct an accurate indoor localization system with an error rate of approximately 1.1 m. The KNN algorithm has been adopted for its simplicity. To eliminate access point installation and the infrastructure, PDR is proposed.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

et al. 2019

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Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

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“…An initial localization module, a fusion algorithm combined with a kNN and least square algorithms provided the initial localization. However, the study provided the basic location estimation through PDR [61].…”

Section: Rss Of Bluetooth and The Inertial Sensors <1 Mmentioning

confidence: 99%

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

et al. 2019

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The number of studies on the development of indoor positioning systems has increased recently due to the growing demands of the various location-based services. Inertial sensors available in commercial smartphones play an important role in indoor localization and navigation owing to their highly accurate localization performance. In this study, the inertial sensors of a smartphone, which generate distinct patterns for physical activities and action units (AUs), are employed to localize a target in an indoor environment. These AUs, (such as a left turn, right turn, normal step, short step, or long step), help to accurately estimate the indoor location of a target. By taking advantage of sophisticated deep learning algorithms, we propose a novel approach for indoor navigation based on long short-term memory (LSTM). The LSTM accurately recognizes physical activities and related AUs by automatically extracting the efficient features from the distinct patterns of the input data. Experiment results show that LSTM provides a significant improvement in the indoor positioning performance through the recognition task. The proposed system achieves a better localization performance than the trivial fingerprinting method, with an average error of 0.782 m in an indoor area of 128.6 m2. Additionally, the proposed system exhibited robust performance by excluding the abnormal activity from the pedestrian activities.

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Unsupervised Indoor Localization Based on Smartphone Sensors, iBeacon and Wi-Fi

Cited by 30 publications

References 32 publications

Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Real-Time Indoor Positioning Approach Using iBeacons and Smartphone Sensors

Sensors of Smart Devices in the Internet of Everything (IoE) Era: Big Opportunities and Massive Doubts

Indoor Positioning System: A New Approach Based on LSTM and Two Stage Activity Classification

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