Deep Neural Networks for wireless localization in indoor and outdoor environments

Zhang, Wei; Liu, Kan; Zhang, Weidong; Zhang, Youmei; Gu, Jason

doi:10.1016/j.neucom.2016.02.055

Cited by 245 publications

(143 citation statements)

References 27 publications

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“…Unlike traditional solutions relying on complex filtering and timeconsuming parameter tuning specific to given conditions, the popular DNNs can provide attractive solutions to Wi-Fi fingerprinting due to their less parameter tuning and adaptability to a wider range of conditions with standard architectures and training algorithms [9][10][11]: In [9], a four-layer DNN generates a coarse positioning estimate, which, in turn, is refined to produce a final position estimate by a hidden Markov model (HMM)-based fine localizer. The performance of the proposed indoor localization system is evaluated in both indoor and outdoor environments which are divided into hundreds of square grids.…”

Section: Wi-fi Fingerprinting Based On Deep Neural Networkmentioning

confidence: 99%

“…Considering all the floors within each building and the locations on each floor, the total number of distinct locations (e.g., offices, lecture rooms, and labs) is already on the order of thousands. If we adopt a grid-based representation of the localization area as in [9], the total number of locations would be even greater. The indoor localization system to cover such a large building complex, therefore, must be scalable.…”

Section: Wi-fi Fingerprintingmentioning

confidence: 99%

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A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

2018

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Background:One of the key technologies for future large-scale location-aware services covering a complex of multi-story buildings is a scalable indoor localization technique. In this paper, we report the current status of our investigation on the use of deep neural networks (DNNs) for the scalable building/floor classification and floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the hierarchical nature of the building/floor estimation and floor-level coordinates estimation of a location, we propose a new DNN architecture consisting of a stacked autoencoder for the reduction of feature space dimension and a feed-forward classifier for multi-label classification of building/floor/location, on which the multi-building and multi-floor indoor localization system based on Wi-Fi fingerprinting is built. Results: We evaluate the performance of building/floor estimation and floor-level coordinates estimation of a given location using the UJIIndoorLoc dataset covering three buildings with four or five floors in the Jaume I University (UJI) campus, Spain. Experimental results demonstrate the feasibility of the proposed DNN-based indoor localization system, which can provide near state-of-the-art performance using a single DNN. Conclusions:The proposed scalable DNN architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting can achieve near state-of-the-art performance with just a single DNN and enables the implementation with lower complexity and energy consumption at mobile devices.

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Section: Wi-fi Fingerprinting Based On Deep Neural Networkmentioning

confidence: 99%

Section: Wi-fi Fingerprintingmentioning

confidence: 99%

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

2018

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“…DNN can provide accurate Wi-Fi-based indoor localization due to the ability to learn signal fluctuations through time and environmental dynamicity because of its deeper functions that map the input to the output [4,9,10,15,16]. In [4], a stacked denoising auto-encoder (SDA) was used to reduce the dimensions, and then the hidden Markov model (HMM) was applied to refine the localization. The root mean square (RMS) was compared with respect to different sample sizes, and shows that using SDA and HMM has better accuracy than using DNN and HMM alone.…”

Section: Related Workmentioning

confidence: 99%

“…In indoor environments; Global Positioning System (GPS), Global Navigation Satellite System (GLONASS), and Galileo are not practical because they lack line of sight (LoS) between the satellites and the receivers, which is easily affected by the physical layout of equipment and is sensitive to occlusion [3][4][5]. Therefore, indoor localization becomes common in indoor environments to offer convenient services.…”

Section: Introductionmentioning

confidence: 99%

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

et al. 2018

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Featured Application: This work can be applied to track mobile users, manage indoor navigations, provide alarms in secured areas, such as unacceptable hospital areas, military systems and mass rapid transit (MRT) inside enclosed areas. In general, this work is applicable to inside enclosed areas where the specific location is mandatory.Abstract: In the Internet of Things (IoT) era, indoor localization plays a vital role in academia and industry. Wi-Fi is a promising scheme for indoor localization as it is easy and free of charge, even for private networks. However, Wi-Fi has signal fluctuation problems because of dynamic changes of environments and shadowing effects. In this paper, we propose to use a deep neural network (DNN) to achieve accurate localization in Wi-Fi environments. In the localization process, we primarily construct a database having all reachable received signal strengths (RSSs), and basic service set identifiers (BSSIDs). Secondly, we fill the missed RSS values using regression, and then apply linear discriminant analysis (LDA) to reduce features. Thirdly, the 5-BSSIDs having the strongest RSS values are appended with reduced RSS vector. Finally, a DNN is applied for localizing Wi-Fi users. The proposed system is evaluated in the classification and regression schemes using the python programming language. The results show that 99.15% of the localization accuracy is correctly classified. Moreover, the coordinate-based localization provides 50%, 75%, and 93.10% accuracies for errors less than 0.50 m, 0.75 m, and 0.90 m respectively. The proposed method is compared with other algorithms, and our method provides motivated results. The simulation results also show that the proposed method can robustly localize Wi-Fi users in hierarchical and complex wireless environments.

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“…To evaluate the performance of fingerprint-based autoencoder network scheme, we compare the performance of the proposed scheme utilizing the autoencoder network to extract features from the coarse RSS values with Horus system [20] and Zhang's method [21]. Horus system is a typical method leveraging coarse RSS values and estimating the target location in a probability-based manner.…”

Section: Performance Comparisonmentioning

confidence: 99%

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

Mei

2018

MATEC Web Conf.

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Abstract. Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

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Deep Neural Networks for wireless localization in indoor and outdoor environments

Cited by 245 publications

References 27 publications

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

A scalable deep neural network architecture for multi-building and multi-floor indoor localization based on Wi-Fi fingerprinting

Applying Deep Neural Network (DNN) for Robust Indoor Localization in Multi-Building Environment

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

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