A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Torres-Sospedra, Joaquín; Richter, Philipp; Moreira, Adriano; Mendoza-Silva, Germán Martín; Lohan, Elena Simona; Trilles, Sergio; Matey-Sanz, Miguel; Huerta, Joaquín

doi:10.1109/tmc.2020.3017176

Cited by 39 publications

(75 citation statements)

References 74 publications

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“…The matching can be based on various Machine Learning (ML) approaches. The most commonly used fingerprinting algorithm is the k-Nearest Neighbours (kNN) algorithm [60]. FP methods have been addressed in detail in [59], a book dedicated to all aspects involved in FP-based positioning, navigation, and user tracking.…”

Section: User Location Trackingmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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Some of the recent developments in data science for worldwide disease control have involved research of large-scale feasibility and usefulness of digital contact tracing, user location tracking, and proximity detection on users’ mobile devices or wearables. A centralized solution relying on collecting and storing user traces and location information on a central server can provide more accurate and timely actions than a decentralized solution in combating viral outbreaks, such as COVID-19. However, centralized solutions are more prone to privacy breaches and privacy attacks by malevolent third parties than decentralized solutions, storing the information in a distributed manner among wireless networks. Thus, it is of timely relevance to identify and summarize the existing privacy-preserving solutions, focusing on decentralized methods, and analyzing them in the context of mobile device-based localization and tracking, contact tracing, and proximity detection. Wearables and other mobile Internet of Things devices are of particular interest in our study, as not only privacy, but also energy-efficiency, targets are becoming more and more critical to the end-users. This paper provides a comprehensive survey of user location-tracking, proximity-detection, and digital contact-tracing solutions in the literature from the past two decades, analyses their advantages and drawbacks concerning centralized and decentralized solutions, and presents the authors’ thoughts on future research directions in this timely research field.

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Section: User Location Trackingmentioning

confidence: 99%

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Shubina

Holcer

Gould

et al. 2020

Data

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“…Specifically, the proposed method in [92] selects RSSI and CSI as hybrid fingerprint data based on correlation coefficient, so as to construct a reliable fingerprint database, then deep learning approach is applied to further improve the accuracy. In addition, another work [93] provides a comprehensive comparison of existing clustering models in fingerprinting. The authors applied two aggregated normalized metrics as well as 16 heterogeneous datasets to evaluate the accuracy and computational cost of the clustering methods such as C-means clustering and Affinity Propagation Clustering (APC), with regard to conventional clustering methods as KNN and K-means.…”

Section: Machine Learning (Ml)-based Methodsmentioning

confidence: 99%

A Survey of Recent Indoor Localization Scenarios and Methodologies

Tian

Cabani

Chafouk

2021

Sensors

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Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

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“…This leaves the RM as the only input that can increase the computational cost of the system. For larger spaces, the number of samples in the RM may increase, however, this problem can be minimized by applying a clustering or filtering technique [30] which allows the reduction of RM search in large areas without compromising the accuracy. As reported in [30] there are techniques that effectively reduced the RM size while improving the accuracy in some cases.…”

Section: Computational Complexitymentioning

confidence: 99%

TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments

Silva

Pendão

Torres-Sospedra³

et al. 2022

IEEE Trans. Syst. Man Cybern, Syst.

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Localization and tracking of industrial vehicles have a key role in increasing productivity and improving the logistics processes of factories. Due to the demanding requirements of industrial vehicle tracking and navigation, existing systems explore technologies, such as LiDAR or ultra wide-band to achieve low positioning errors. In this article we propose TrackInFactory, a system that combines Wi-Fi with motion sensors, achieving submeter accuracy and a low maximum error. A tight coupling approach is explored in sensor fusion with a particle filter (PF). Information regarding the vehicle's initial position and heading is not required. This approach uses the similarity of Wi-Fi samples to update the particles' weights as they move according to motion sensor data. The PF dynamically adjusts its parameters based on a metric for estimating the confidence in position estimates, allowing to improve positioning performance. A series of simulations were performed to tune the PF. Then the approach was validated in real-world experiments with an industrial tow tractor, achieving a mean error of 0.81 m. In comparison to a loose coupling approach, this method reduced the maximum error by more than 60% and improved the overall mean error by more than 20%.

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A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Cited by 39 publications

References 74 publications

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

Survey of Decentralized Solutions with Mobile Devices for User Location Tracking, Proximity Detection, and Contact Tracing in the COVID-19 Era

A Survey of Recent Indoor Localization Scenarios and Methodologies

TrackInFactory: A Tight Coupling Particle Filter for Industrial Vehicle Tracking in Indoor Environments

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