From one to crowd: a survey on crowdsourcing-based wireless indoor localization

Zhou, Xiaolei; Chen, Tao; Guo, Deke; Teng, Xiaoqiang; Yuan, Bo

doi:10.1007/s11704-017-6520-z

Cited by 34 publications

(23 citation statements)

References 59 publications

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“…With the popularity of wireless networks and advances in orthogonal frequency division multiplexing (OFDM) technology, the traditional landscape has changed [31]. Channel responses can be extracted from commercial Wi-Fi devices under 802.11/g/n standards, which reveals that a set of channel measurements depict the amplitude and phase of each subcarrier [44]. Because RSSI cannot distinguish multiple signal propagation paths one by one [45], different multipath propagation conditions may lead to different sensitivities based on human movement, which further affects the results of human detection [46].…”

Section: Csi Data Collectionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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Human behavior detection has become increasingly significant in various fields of application. In this paper, we propose a device-free indoor human behavior detection method with channel state information (CSI) and principal component analysis (PCA), respectively, in the line of sight environment, non-line-of-sight environment, and through the wall environment experiments. We divide this method into two parts. It begins with an online phase. A fingerprint database is established by collecting the original data packets of CSI in different time periods and using the characteristics of PCA algorithm to reduce the dimension of the original CSI data. Then, some outlier values are removed by Kalman filter algorithm, and we will get more stable data and fully prepared for the docking experiments. At the same time, the PCA algorithm's estimation results are corrected according to the detected real-time motion speed to reduce the mismatch target. Then, in the offline phase, the classification of data is collected in the real-time environment by using support vector machine (SVM) algorithm. This method not only reduces the time complexity of the algorithm but also improves the detection rate of the human's behavior and reduces the error. The processed data are matched with the data in the fingerprint database, and finally, the detection of different behaviors performed by humans in an indoor environment is finally achieved according to the matching results. We experimented repeatedly in three different scenarios, with an average 95% of human behavior detection rate in three different environments. In addition, we compare the method proposed in this paper with the existing methods in different aspects, such as the impact of the number of subcarriers, the impact of data packets, and the impact of the test area. The experimental results show that this method is superior to other algorithms in terms of average error and indoor activity recognition accuracy, which can more accurately identify indoor human motion behavior and improve the stability of the system.

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Section: Csi Data Collectionmentioning

confidence: 99%

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Dang

Huang

Hao

et al. 2018

J Wireless Com Network

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“…The location of a fingerprint can be estimated using the known location of similar fingerprints previously recorded. One of the critical challenges to support such fingerprinting localization is to create and maintain accurate fingerprint databases [4]. A site survey is often used to collect fingerprints from a targeted area.…”

Section: Introductionmentioning

confidence: 99%

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

2019

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Radio Frequency (RF) fingerprinting is widely applied for indoor positioning due to the existing Wi-Fi infrastructure present in most indoor spaces (home, work, leisure, among others) and the widespread usage of smartphones everywhere. It corresponds to a simple idea, the signal signature in a location tends to be stable over the time. Therefore, with the signals received from multiple APs, a unique fingerprint can be created. However, the Wi-Fi signal is affected by many factors which degrade the positioning error range to around a few meters. This paper introduces a collaborative method based on device-to-device (D2D) communication to improve the positioning accuracy using only fingerprinting and the direct communication to nearby devices. The results presented in this paper show that the positioning error can be reduced around 44% by considering D2D communication in the operational stage without adding new infrastructure for fingerprinting or complex resource-consuming filters. Moreover, the presence of very large errors is significantly reduced when the collaborative positioning based on D2D is available.

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“…The location of a test fingerprint can be estimated to a known location with minimal signal difference. One of the key challenges to support such fingerprinting localization is to construct an indoor radio map in the offline training phase [ 5 , 6 , 7 , 8 ]. Normally, the indoor environment is divided into non-overlapping grid cells.…”

Section: Introductionmentioning

confidence: 99%

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Lin

Wang

Yang

et al. 2018

Sensors

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Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.

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From one to crowd: a survey on crowdsourcing-based wireless indoor localization

Cited by 34 publications

References 59 publications

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

PCA-Kalman: device-free indoor human behavior detection with commodity Wi-Fi

Improving RF Fingerprinting Methods by Means of D2D Communication Protocol

Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

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