Enhancing RFID indoor localization with cellular technologies

Aguilar-Garcı́a, Alejandro; Fortes, Sergio; Colin, Elizabeth; Barco, Raquel

doi:10.1186/s13638-015-0444-9

Cited by 26 publications

(10 citation statements)

References 23 publications

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“…Conversely, the improvement of User Equipment (UE) positioning in both outdoors and indoors [ 3 , 4 , 5 , 6 ], allows for the generation and application of position information for operations, administration and maintenance (OAM) automatic mechanisms [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

Fortes

Baena

Villegas

et al. 2021

Sensors

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Recent years have seen the proliferation of different techniques for outdoor and, especially, indoor positioning. Still being a field in development, localization is expected to be fully pervasive in the next few years. Although the development of such techniques is driven by the commercialization of location-based services (e.g., navigation), its application to support cellular management is considered to be a key approach for improving its resilience and performance. When different approaches have been defined for integrating location information into the failure management activities, they commonly ignore the increase in the dimensionality of the data as well as their integration into the complete flow of networks failure management. Taking this into account, the present work proposes a complete integrated approach for location-aware failure management, covering the gathering of network and positioning data, the generation of metrics, the reduction in the dimensionality of such data, and the application of inference mechanisms. The proposed scheme is then evaluated by system-level simulation in ultra-dense scenarios, showing the capabilities of the approach to increase the reliability of the supported diagnosis process as well as reducing its computational cost.

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Section: Introductionmentioning

confidence: 99%

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

Fortes

Baena

Villegas

et al. 2021

Sensors

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“…Many indoor localization methods have been proposed in academia as well as in industry. Typical indoor localization methods like Wi-Fi [4][5][6][7][8][9], Bluetooth [10], ultrasound (US) [11], infrared (IR) [12], radio frequency identification (RFID) [13,14], magnetic field (MF) [15], and ultra-wideband (UWB) [16] methods, have been investigated. Indoor positioning systems estimate the location by using different types of measurements, such as angle of arrival (AOA), time of arrival (TOA), time difference of arrival (TDOA), and received signal strength (RSS).…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Wang

Fan

et al. 2020

Mobile Information Systems

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Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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“…Therein, the common examples of infrastructure-based technologies are Radio Frequency IDentification (RFID), Ultra-WideBand (UWB), Infra-Red (IR) and Global Positioning Systems (GPS) [1,2,3,4]. Fusion of these technologies is also reported in [5] where RFID is utilized along with cellular data. The frequencies from Ultra High-Frequency RFID (UHF RFID) and macro-cellular networks are used to refine the localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Floor Identification Using Magnetic Field Data with Smartphone Sensors

Ashraf

Hur

Shafiq

et al. 2019

Sensors

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Floor identification plays a key role in multi-story indoor positioning and localization systems. Current floor identification systems rely primarily on Wi-Fi signals and barometric pressure data. Barometric systems require installation of additional standalone sensors to perform floor identification. Wi-Fi systems, on the other hand, are vulnerable to the dynamic environment and adverse effects of path loss, shadowing, and multipath fading. In this paper, we take advantage of a pervasive magnetic field to compensate for the limitations of these systems. We employ smartphone sensors to make the proposed scheme infrastructure free and cost-effective. We use smartphone magnetic sensors to identify the floors in a multi-story building with improved accuracy. Floor identification is performed with user activities of normal walking, call listening, and phone swinging. Various machine learning techniques are leveraged to identify user activities. Extensive experiments are performed to evaluate the proposed magnetic-data-based floor identification scheme. Additionally, the impact of device heterogeneity on floor identification is investigated using Samsung Galaxy S8, LG G6, and LG G7 smartphones. Research results demonstrate that the magnetic floor identification outperforms barometric and Wi-Fi-enabled floor detection techniques. A floor change module is incorporated to further enhance the accuracy of floor identification.

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Enhancing RFID indoor localization with cellular technologies

Cited by 26 publications

References 23 publications

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

Location-Awareness for Failure Management in Cellular Networks: An Integrated Approach

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Floor Identification Using Magnetic Field Data with Smartphone Sensors

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