Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Wang, Yifan; Gao, Jingxiang; Li, Zengke; Zhao, Long

doi:10.3390/app10010321

Cited by 26 publications

(11 citation statements)

References 32 publications

(41 reference statements)

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“…If the characteristic information received by the wireless signals of different spatial geographic locations is used as the fingerprint of the current geographic location, the similarity between the fingerprint and the established spatial fingerprint database can be compared to obtain the coordinates of the spatial geographic location to be measured. This method does not need to understand the distance relationship between the AP point and the mobile terminal device but only needs to collect the fingerprint signal of the reference point in advance to establish a fingerprint database and perform positioning according to the AP intensity value to match [17]. The schematic diagram of the location fingerprint positioning principle is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Zou

Xingrui

Zeren

2022

Wireless Communications and Mobile Computing

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To study the intelligent sensing and positioning technology of the Internet of Things (IoT) combined with the K -nearest neighbor algorithm, the K -nearest neighbor matching algorithm and optimization algorithm are introduced using the indoor Wi-Fi positioning technology. The study proposes weighting K -nearest neighbor (WKNN) by weighted Euclidean distance, adaptive weighted Euclidean distance K-nearest neighbor Wi-Fi localization algorithm, and optimal K -value Wi-Fi fingerprint localization algorithm. The experimental error is verified. The experimental results show that the lowest error of continuous acquisition of 3 s signal values in experimental environment A is 1.8815 m, which is 10.13% lower than the error of only acquiring 1 s for the same K -value. The lowest error of environment B scheme two can reach 1.8862, which is 7.06% lower than the error of the same K -value. The optimal K -value Wi-Fi fingerprint positioning algorithm by distance constraint has better positioning accuracy than other KNN positioning algorithms, and the positioning fluctuation is smaller. The average positioning error of the optimal K in environment A is 1.2987 m, which is 0.2797 m less than the average of the traditional positioning algorithm. In environment B, the average positioning error of the optimal K is 1.5353 m, which is 0.3253 m less than the average of the traditional positioning algorithm. Therefore, the optimal K -value Wi-Fi positioning algorithm proposed has better performance.

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Section: Methodsmentioning

confidence: 99%

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Zou

Xingrui

Zeren

2022

Wireless Communications and Mobile Computing

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“…The proposed system in [8] is based on a deep neural network. The extracted importance features using stacked denoising autoencoder is exploited to reconstruct radio map.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighted autoencoder extreme learning machine

Alitalesi

Jazayeriy

Kazemitabar

2022

Comput. Sci. Inf. Technol.

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In practical applications, accurate floor determination in multi-building/floor environments is particularly useful and plays an increasingly crucial role in the performance of location-based services. An accurate and robust building and floor detection can reduce the location search space and ameliorate the positioning and wayfinding accuracy. As an efficient solution, this paper proposes a floor identification method that exploits statistical properties of wireless access point propagated signals to exponent received signal strength (RSS) in the radio map. Then, using single-layer extreme learning machine-weighted autoencoder (ELM-WAE) main feature extraction and dimensional reduction is implemented. Finally, ELM based classifier is trained over a new feature space to determine floor level. For the efficiency evaluation of our proposed model, we utilized three different datasets captured in the real scenarios. The evaluation result shows that the proposed model can achieve state-of-art performance and improve the accuracy of floor detection compared with multiple recent techniques. In this way, the floor level can be identified with 97.30%, 95.32%, and 96.39% on UJIIndoorLoc, Tampere, and UTSIndoorLoc datasets, respectively.

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“…Deep Neural Networks (DNN) have attracted researchers’ attention in recent years due to their great success when applied to complex problems. The authors in [ 28 ] used a stacked denoising auto-encoder (SDAE) for indoor localization purposes. To improve the results given by the SDAE, two filters were used: (a) a dynamic Kalman filter to take into account the speed of the user, and (b) an HMM to model transitions between fingerprint points.…”

Section: Previous Workmentioning

confidence: 99%

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Belmonte-Fernández

Sansano-Sansano

Miedes

et al. 2021

Sensors

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Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

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Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Cited by 26 publications

References 32 publications

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighted autoencoder extreme learning machine

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

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Robust and Accurate Wi-Fi Fingerprint Location Recognition Method Based on Deep Neural Network

Cited by 26 publications

References 32 publications

Intelligent Perception and Positioning Technology of Internet of Things by K -Nearest Neighbor Matching Algorithm

Intelligent Perception and Positioning Technology of Internet of Things by K -Nearest Neighbor Matching Algorithm

Wi-Fi fingerprinting-based floor detection using adaptive scaling and weighted autoencoder extreme learning machine

A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

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Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm

Intelligent Perception and Positioning Technology of Internet of Things by $K$ -Nearest Neighbor Matching Algorithm