Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning

Nowicki, Michał; Wietrzykowski, Jan

doi:10.1007/978-3-319-54042-9_57

Cited by 121 publications

(98 citation statements)

References 15 publications

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“…Battiti et al [63] used a multi-layer perceptron for indoor positioning, with a positioning accuracy of 2.3 m. Nowicki et al [64] proposed a method that used Deep Neural Network (DNN) combined with stacked autoencoder (SAE). The DNN used in this method was the first to be used for Wi-Fi fingerprinting and could achieve better performance in image analysis than other methods.…”

Section: (3) Artificial Neural Networkmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

186

125

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Abstract:The widely applied location-based services require a high standard for positioning technology. Currently, outdoor positioning has been a great success; however, indoor positioning technologies are in the early stages of development. Therefore, this paper provides an overview of indoor fingerprint positioning based on Wi-Fi. First, some indoor positioning technologies, especially the Wi-Fi fingerprint indoor positioning technology, are introduced and discussed. Second, some evaluation metrics and influence factors of indoor fingerprint positioning technologies based on Wi-Fi are introduced. Third, methods and algorithms of fingerprint indoor positioning technologies are analyzed, classified, and discussed. Fourth, some widely used assistive positioning technologies are described. Finally, conclusions are drawn and future possible research interests are discussed. It is hoped that this research will serve as a stepping stone for those interested in advancing indoor positioning.

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Section: (3) Artificial Neural Networkmentioning

confidence: 99%

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Xia

Liu

Yuan

et al. 2017

IJGI

186

125

View full text Add to dashboard Cite

show abstract

“…As discussed in [9], DNN shows immunity against signal fluctuations, noise effects, device dependency, and the elimination of time-consuming manual parameter tuning. According to [10,14], DNN helps to lower the workforce burden of localization. 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].…”

Section: Related Workmentioning

confidence: 99%

“…According to [10,14], DNN helps to lower the workforce burden of localization. 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.…”

Section: Related Workmentioning

confidence: 99%

“…In [10], a DNN system was used for building and floor classification on the publicly-available UJIIndoorLoc dataset. This work used a stacked auto-encoder for feature reduction.…”

Section: Related Workmentioning

confidence: 99%

“…Based on discussions in [10], there are two methods to estimate Wi-Fi-based localization: Wi-Fi ranging and Wi-Fi fingerprinting. In Wi-Fi ranging, estimation is done directly with the distance towards the APs, which is impractical inside buildings due to multiple signal occlusions, wall reflections, and the overall influence of people.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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DRVAT: Exploring RSSI series representation and attention model for indoor positioning

Sun

Tao

et al. 2021

Int J of Intelligent Sys

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Although Bluetooth Low Energy (BLE) fingerprinting localization has become a hot research topic with encouraging results, it is difficult to predict the location depending on a short duration received signal strength indication (RSSI) sequence in realistic scenarios due to the severe fluctuation of RSSI. We introduce a new perspective to view the indoor positioning problem by radio map fingerprint. We argue that even though beacons may be independently deployed, the RSSI series bear certain spatial relation because of their copresence in the same physical space. The latent relation implicitly conveyed by the coexistence of their signals at various indoor locations. Unlike existing approaches that try to find a direct mapping between sensed signals and the corresponding location, we explore the spatial relation of beacons from the input data to estimate location. We propose a deep learning localization system, termed DRVAT, which is based on the distributed representation vector (DRV) and selfattention (AT) among the pairs of MAC-RSSI. First, we obtain DRVs which represent dense features in low dimensionality through pre-training on all MAC-RSSIs. Then we exploit self-attention mechanism to learn the latent spatial relation of beacons. Finally, MAC-RSSIs labeled with locations are used to fine-tune the model for estimating location. Localization accuracy results

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Low-Effort Place Recognition with WiFi Fingerprints Using Deep Learning

Cited by 121 publications

References 15 publications

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

Indoor Fingerprint Positioning Based on Wi-Fi: An Overview

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

DRVAT: Exploring RSSI series representation and attention model for indoor positioning

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