Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Zhang, Chenbin; Qin, Ningning; Xue, Yusheng; Yang, Le

doi:10.3390/s20041067

Cited by 38 publications

(20 citation statements)

References 31 publications

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“…However, the reduction in the computational load often comes with accuracy issues [ 31 ]. These advances of the area resulted in different approaches concerning traditional clustering such as K -means [ 32 ], hierarchical [ 33 , 34 ], and other novel clustering techniques [ 35 ]. Graph-based data structures have also been proposed recently to improve IPSs.…”

Section: Related Workmentioning

confidence: 99%

A Small World Graph Approach for an Efficient Indoor Positioning System

Lima

Guimarães²,

Santos

et al. 2021

Sensors

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The main goal of an Indoor Positioning System (IPS) is to estimate the position of mobile devices in indoor environments. For this purpose, the primary source of information is the signal strength of packets received by a set of routers. The fingerprint technique is one of the most used techniques for IPSs. By using supervised machine learning techniques, it trains a model with the received signal intensity information so it can be used to estimate the positions of the devices later in an online phase. Although the k-Nearest Neighbors (kNN) is one of the most widely used classification methods due to its accuracy, it has no scalability since a sample that needs to be classified must be compared to all other samples in the training database. In this work, we use a novel hierarchical navigable small world graph technique to build a search structure so the location of a sample can be efficiently found, allowing the IPSs to be used in large-scale scenarios or run on devices with limited resources. To carry out our performance evaluation, we proposed a synthetic IPS dataset generator as well as implemented a complete real-world, large-scale IPS testbed. We compared the performance of our graph-based solution with other known kNN variants, such as Kd-Tree and Ball-Tree. Our results clearly show the performance gains of the proposed solution at 98% when compared to the classic kNN and at least 80% when compared to tree-based approaches.

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Section: Related Workmentioning

confidence: 99%

A Small World Graph Approach for an Efficient Indoor Positioning System

Lima

Guimarães²,

Santos

et al. 2021

Sensors

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show abstract

“…Interestingly, SVM can also be tweaked to solve problem of more than two classes. one of the methods of classifying multiple classes using SVM is One-versus-One (OVO) [36] classification strategy which we adopted in this study. Using OVO, the number of classes ( N c ) are broken down into multiple binary classification problem.…”

Section: Choice Of a Classifier (Predictor)mentioning

confidence: 99%

Burns Depth Assessment Using Deep Learning Features

et al. 2020

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Purpose Burns depth evaluation is a lifesaving task and very challenging that requires objective techniques to accomplish. While the visual assessment is the most commonly used by surgeons, its accuracy reliability ranges between 60 and 80% and subjective that lacks any standard guideline. Currently, the only standard adjunct to clinical evaluation of burn depth is Laser Doppler Imaging (LDI) which measures microcirculation within the dermal tissue, providing the burns potential healing time which correspond to the depth of the injury achieving up to 100% accuracy. However, the use of LDI is limited due to many factors including high affordability and diagnostic costs, its accuracy is affected by movement which makes it difficult to assess paediatric patients, high level of human expertise is required to operate the device, and 100% accuracy possible after 72 h. These shortfalls necessitate the need for objective and affordable technique. Method In this study, we leverage the use of deep transfer learning technique using two pretrained models ResNet50 and VGG16 for the extraction of image patterns (ResFeat50 and VggFeat16) from a a burn dataset of 2080 RGB images which composed of healthy skin, first degree, second degree and third-degree burns evenly distributed. We then use One-versus-One Support Vector Machines (SVM) for multi-class prediction and was trained using 10-folds cross validation to achieve optimum trade-off between bias and variance. Results The proposed approach yields maximum prediction accuracy of 95.43% using ResFeat50 and 85.67% using VggFeat16. The average recall, precision and F1-score are 95.50%, 95.50%, 95.50% and 85.75%, 86.25%, 85.75% for both ResFeat50 and VggFeat16 respectively. Conclusion The proposed pipeline achieved a state-of-the-art prediction accuracy and interestingly indicates that decision can be made in less than a minute whether the injury requires surgical intervention such as skin grafting or not.

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“…Zhang et al [16] proposed an algorithm based on hierarchical classification and k-means. The improved k-means is used to divide the indoor environment into overlapping zones.…”

Section: Related Workmentioning

confidence: 99%

Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices

Quezada-Gaibor

Klus

Torres-Sospedra

et al. 2020

2020 12th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT)

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IoT devices and wearables may rely on Wi-Fi fingerprinting to estimate the position indoors. The limited resources of these devices make it necessary to provide adequate methods to reduce the operational computational load without degrading the positioning error. Thus, the aim of this article is to improve the positioning error and reduce the dimensionality of the radio map by using an enhanced DBSCAN. Moreover, we provide an additional analysis of combining DBSCAN + PCA analysis for further dimensionality reduction. Thereby, we implement a postprocessing method based on the correlation coefficient to join "noisy" samples to the formed clusters with Density-based Spatial Clustering of Applications with Noise (DBSCAN). As a result, the positioning error was reduced by 10% with respect to the plain DBSCAN, and the radio map dimensionality was reduced in both dimensions, samples and Access Points (APs).

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Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Cited by 38 publications

References 31 publications

A Small World Graph Approach for an Efficient Indoor Positioning System

A Small World Graph Approach for an Efficient Indoor Positioning System

Burns Depth Assessment Using Deep Learning Features

Improving DBSCAN for Indoor Positioning Using Wi-Fi Radio Maps in Wearable and IoT Devices

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