Supplementary open dataset for WiFi indoor localization based on received signal strength

Bi, Jingxue; Wang, Yunjia; Yu, Baoguo; Cao, Hongji; Shi, Tongguang; Lu, Haojie

doi:10.1186/s43020-022-00086-y

Cited by 25 publications

(6 citation statements)

References 34 publications

(36 reference statements)

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“…During our investigation of the effects of various aspects of MOGP-based data augmentation on localization performance, we focused our investigation of MOGP on the linear models of ICM and LMC and based the experiments only on the UJIIndoorLoc database. Our investigation in this paper, therefore, could be extended to other MOGP models with kernels better suited for indoor localization and multi-building and multi-floor databases (e.g., [46,47]).…”

Section: Discussionmentioning

confidence: 99%

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Tang,

Li,

Kim

et al. 2024

Sensors

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Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.

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Section: Discussionmentioning

confidence: 99%

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Tang,

Li,

Kim

et al. 2024

Sensors

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“…To establish the relationship between error distributions and corresponding scenario configurations, localization errors are investigated by using five public datasets [36], [49], i.e. LAB, OFFICE, CETC, HCXY and SYL, and one self-collected dataset WiCAM (see Section 6.1).…”

Section: Empirical Distributions Of Localization Errorsmentioning

confidence: 99%

On the Fine-Grained Crowd Analysis via Passive WiFi Sensing

Hao,

Huang,

Jia

et al. 2024

IEEE Trans. on Mobile Comput.

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Regarding the passive WiFi sensing based crowd analysis, this paper first theoretically investigates its limitations, and then proposes a deep learning based scheme targeted for returning fine-grained crowd states in large surveillance areas. To this end, three key challenges are coped with: to relieve the influences of the randomness and sparsity induced by passive WiFi sensing, an attention-based deep convolutional autoencoder model is designed to recover accurate crowd density maps in a way similar to image reconstruction; to combat the anonymity caused by MAC randomization, following the identification of local high-density crowds (LHDCs) with the density clustering algorithm, i.e. DM-DBSCAN, a bidirectional convolutional LSTM based model is employed to infer LHDC speeds; to overcome the absence of passive WiFi sensing datasets for model training, three semi-synthetic datasets are produced by emulating passive WiFi sensing with practical pedestrian tracking datasets. Extensive experiments confirm that, the proposed scheme significantly outperforms existing WiFi-based methods in terms of crowd density estimation and provides superior crowd speed estimation. More importantly, the scheme can also produce consistent crowd states on a real-world dataset, revealing that it has the ability to support accurate, visualized and real-time crowd monitoring in large surveillance areas.

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“…As a supplement to the UJIIndoorLoc database, Bi et al presented the SODIndoorLoc database, which covers three buildings with one or three floors, amounting to a total area of 8000 m 2 , and provides 23,925 Wi-Fi RSS samples from 105 single-band and dual-band WAPs measured at 1630 RPs and 272 testing points (TPs) with 21,205 samples for training and 2720 samples for testing and validation [ 24 ].…”

Section: Review Of Publicly Available Wi-fi Rssi Fingerprint Databasesmentioning

confidence: 99%

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

Li,

Tang,

Kim

et al. 2024

Sensors

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Large-scale multi-building and multi-floor indoor localization has recently been the focus of intense research in indoor localization based on Wi-Fi fingerprinting. Although significant progress has been made in developing indoor localization algorithms, few studies are dedicated to the critical issues of using existing and constructing new Wi-Fi fingerprint databases, especially for large-scale multi-building and multi-floor indoor localization. In this paper, we first identify the challenges in using and constructing Wi-Fi fingerprint databases for large-scale multi-building and multi-floor indoor localization and then provide our recommendations for those challenges based on a case study of the UJIIndoorLoc database, which is the most popular publicly available Wi-Fi fingerprint multi-building and multi-floor database. Through the case study, we investigate its statistical characteristics with a focus on the three aspects of (1) the properties of detected wireless access points, (2) the number, distribution and quality of labels, and (3) the composition of the database records. We then identify potential issues and ways to address them using the UJIIndoorLoc database. Based on the results from the case study, we not only provide valuable insights on the use of existing databases but also give important directions for the design and construction of new databases for large-scale multi-building and multi-floor indoor localization in the future.

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Supplementary open dataset for WiFi indoor localization based on received signal strength

Cited by 25 publications

References 34 publications

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization

On the Fine-Grained Crowd Analysis via Passive WiFi Sensing

On the Use and Construction of Wi-Fi Fingerprint Databases for Large-Scale Multi-Building and Multi-Floor Indoor Localization: A Case Study of the UJIIndoorLoc Database

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