Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network

Jang, Jinwoo; Hong, Song‐Nam

doi:10.1109/icufn.2018.8436598

Cited by 72 publications

(45 citation statements)

References 10 publications

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“…In [27], a CSI-based device-free localization algorithm is proposed with deep neural networks. In [28], a CNN-based WiFi fingerprinting method was presented, and it outperformed the DNN-based methods. In this paper, we leverage deep learning by integrating a CNN with SAE for more accurate and efficient localization in a multi-building and multi-floor environment.…”

Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

et al. 2019

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With the ubiquitous deployment of wireless systems and pervasive availability of smart devices, indoor localization is empowering numerous location-based services. With the established radio maps, WiFi fingerprinting has become one of the most practical approaches to localize mobile users. However, most fingerprint-based localization algorithms are computation-intensive, with heavy dependence on both offline training phase and online localization phase. In this paper, we propose CNNLoc, a Convolutional Neural Network (CNN) based indoor localization system with WiFi fingerprints for multi-building and multi-floor localization. Specifically, we devise a novel classification model and a novel positioning model by combining a Stacked Auto-Encoder (SAE) with a one-dimensional CNN. The SAE is utilized to precisely extract key features from sparse Received Signal Strength (RSS) data while the CNN is trained to effectively achieve high accuracy in the positioning phase. We evaluate the proposed system on the UJIIndoorLoc dataset and Tampere dataset and compare the performance with several state-of-the-art methods. Moreover, we further propose a newly collected WiFi fingerprinting dataset UTSIndoorLoc and test the positioning model of CNNLoc on it. The results show CNNLoc outperforms the existing solutions with 100% and 95% success rates on building-level localization and floor-level localization, respectively.

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Section: Related Workmentioning

confidence: 99%

A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

et al. 2019

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“…Jang et al [29] presented robust image classification of the change in input data caused by the indoor multipath, where they built a 2D virtual radio map from the original 1-D Wi-Fi RSSI signal values and then constructed a CNN using 2-D radio maps as inputs. Channel state information (CSI)-based methods, such as [30][31][32][33][34], have proposed several ideas to process the CSI from Wi-Fi-based orthogonal frequency division modulation (OFDM) signals using deep CNNs. They fed the CSI directly into a CNN to train the position [30,31], train using phase information [32], directly estimate the angle of arrival with a CNN using phase fingerprinting [33], and combine these ideas [34].…”

Section: Related Workmentioning

confidence: 99%

“…Channel state information (CSI)-based methods, such as [30][31][32][33][34], have proposed several ideas to process the CSI from Wi-Fi-based orthogonal frequency division modulation (OFDM) signals using deep CNNs. They fed the CSI directly into a CNN to train the position [30,31], train using phase information [32], directly estimate the angle of arrival with a CNN using phase fingerprinting [33], and combine these ideas [34]. However, the difference between their approaches and ours lies in the nature of the underlying signals and the system setup.…”

Section: Related Workmentioning

confidence: 99%

Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Sinha

Hwang

2019

Electronics

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The intelligent use of deep learning (DL) techniques can assist in overcoming noise and uncertainty during fingerprinting-based localization. With the rise in the available computational power on mobile devices, it is now possible to employ DL techniques, such as convolutional neural networks (CNNs), for smartphones. In this paper, we introduce a CNN model based on received signal strength indicator (RSSI) fingerprint datasets and compare it with different CNN application models, such as AlexNet, ResNet, ZFNet, Inception v3, and MobileNet v2, for indoor localization. The experimental results show that the proposed CNN model can achieve a test accuracy of 94.45% and an average location error as low as 1.44 m. Therefore, our CNN model outperforms conventional CNN applications for RSSI-based indoor positioning.

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“…This system took 2D RSSI images, where each image was of size (N×K), N was the number of training points, and K was the number of APs. The authors in [42] identified the location of a user (building ID and floor ID) by leveraging RSSI obtained from neighboring APs. From a given 1D RSSI fingerprint associated with a training point, a 2D image was made, adding some dummy values (for example: (23×23), 2D image was constructed from a (520×1) RSSI fingerprint where 520 is the number of APs, adding nine dummy data).…”

Section: Introductionmentioning

confidence: 99%

Deep CNN for Indoor Localization in IoT-Sensor Systems

Njima

Ahriz

Zayani

et al. 2019

Sensors

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Currently, indoor localization is among the most challenging issues related to the Internet of Things (IoT). Most of the state-of-the-art indoor localization solutions require a high computational complexity to achieve a satisfying localization accuracy and do not meet the memory limitations of IoT devices. In this paper, we develop a localization framework that shifts the online prediction complexity to an offline preprocessing step, based on Convolutional Neural Networks (CNN). Motivated by the outstanding performance of such networks in the image classification field, the indoor localization problem is formulated as 3D radio image-based region recognition. It aims to localize a sensor node accurately by determining its location region. 3D radio images are constructed based on Received Signal Strength Indicator (RSSI) fingerprints. The simulation results justify the choice of the different parameters, optimization algorithms, and model architectures used. Considering the trade-off between localization accuracy and computational complexity, our proposed method outperforms other popular approaches.

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Indoor Localization with WiFi Fingerprinting Using Convolutional Neural Network

Cited by 72 publications

References 10 publications

A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

A Novel Convolutional Neural Network Based Indoor Localization Framework With WiFi Fingerprinting

Comparison of CNN Applications for RSSI-based Fingerprint Indoor Localization

Deep CNN for Indoor Localization in IoT-Sensor Systems

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