Conditional probability-based ensemble learning for indoor landmark localization

Zhao, Zhongliang; Carrera, V José Luis; Braun, Torsten; Pan, Zhiyang

doi:10.1016/j.comcom.2019.07.007

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…It achieved the best indoor landmark localization accuracy of almost 97% in office-like environments. This method can provide a basis for accurate indoor positioning [9]. An ensemble model consisting of fuzzy classifier and multi-layer perceptron was proposed for indoor parking localization [10].…”

Section: Related Workmentioning

confidence: 99%

A 3D mobile positioning method based on deep learning for hospital applications

Zhang

Wang

2020

J Wireless Com Network

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In this study, a 3D positioning method is proposed for hospital applications, such as navigation within a hospital building. It employs deep learning algorithms to analyze the received signal strength from cellular networks and Wi-Fi access points in order to estimate the positions of mobile stations. A two-stage deep learning procedure (level classification and location determination) is constructed to obtain the exact position information (building level, longitude, and latitude) in multiple-level buildings. To evaluate the performance of the proposed method, an experiment was conducted in the hospital of Xi’an Polytechnic University. In total, 36,985 records, 42 sampling location points, 28 different cellular networks, and 289 different Wi-Fi access points were considered. A deep learning neural network was trained for the first stage of level classification. Three deep learning neural networks were trained to obtain the distinct location coordinates (longitude and latitude) for three different building levels. To compare the efficacy of heterogeneous networks, three kinds of neural networks with different inputs (only cellular, only Wi-Fi APs, and a conjunction of cellular and Wi-Fi APs) were implemented. The accuracy of level classification was shown to be 100% for only Wi-Fi APs as an input. The average distance error of the location determination for different floors was 0.28 m for only Wi-Fi APs and for the conjunction of Wi-Fi APs and cellular networks in the second stage.

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Section: Related Workmentioning

confidence: 99%

A 3D mobile positioning method based on deep learning for hospital applications

Zhang

Wang

2020

J Wireless Com Network

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“…In order to decrease the location error and execution time, a new deep-learning-based indoor fingerprinting system was designed in [3] and the organized deep-learning approach did not reduce the performance of the above computation. In [4], an ensemble learning scheme was introduced to estimate the room level in the indoor localization of smart buildings. Yet, the designed plan did not minimize the time consumption of indoor localization through dimensionality reduction.…”

Section: Introductionmentioning

confidence: 99%

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Pichaimani¹,

Manjula²

2022

Intelligent Automation &Amp; Soft Computing

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The indoor positioning system comprises portable wireless devices that aid in finding the location of people or objects within the buildings. Identification of the items is through the capacity level of the signal received from various access points (i.e., Wi-Fi routers). The positioning of the devices utilizing some algorithms has drawn more attention from the researchers. Yet, the designed algorithm still has problems for accurate floor planning. So, the accuracy of position estimation with minimum error is made possible by introducing Gaussian Distributive Feature Embedding based Deep Recurrent Perceptive Neural Learning (GDFE-DRPNL), a novel framework. Novel features from the dataset are through two processing stages dimensionality reduction and position estimation. Initially, the essential elements selection using the Gaussian Distributive Feature Embedding technique is the novel framework. The feature reduction process aims to reduce the time consumption and overhead for estimating the location of various devices. In the next stage, employ Deep Recurrent multilayer Perceptive Neural Learning to evaluate the device position with dimensionality reduced features. The proposed Deep-learning approach accurately learns the quality and the signal strength data with multiple layers by applying Deming Regressive Trilateral Positioning Model. As a result, the GDFE-DRPNL framework increases the positioning accuracy and minimizes the error rate. The experimental assessments with various factors such as positioning accuracy minimized by 70% and 60%, computation time minimized by 45% and 55% as well as overhead by 11% and 23% compared with PFRL and two-dimensional localization algorithm. Through the experiment and after analyzing the data, verify that the proposed GDFE-DRPNL algorithm in this paper is better than the previous methods.

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“…The approach combines fuzzy classifier and multilayer perceptron to address the research problem identified. Using the concepts of conditional probability and machine learning algorithms, a novel ensemble approach is used to locate users at room level in a smart building 54 . Guo et al 55 proposed a fuzzy detection system for handling organized rumors through explainable adaptive learning.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment

et al. 2021

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Location estimation is one of the critical requirement for developing smart environment products. Due to huge utilization and accessibility of WiFi infrastructure facility in indoor environments, researchers widely studied this technology to locate users accurately to provide several services instantly. In this research work, a hybrid algorithm namely fuzzy decision tree (FDT) with evolutionary fuzzy clustering methods is adopted for optimal user localization in a closed environment. Here we consider the wireless signal strengths received from the smart phones as predictors and the location of the user as the classification label. The required data for the current research is collected from the physical facility available at an office location in USA. The classification results obtained are promising enough to show that the evolutionary clustering approaches provide good fuzzy clusters for FDT induction with better accuracy.

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Conditional probability-based ensemble learning for indoor landmark localization

Cited by 7 publications

References 13 publications

A 3D mobile positioning method based on deep learning for hospital applications

A 3D mobile positioning method based on deep learning for hospital applications

A Machine-Learning Framework to Improve Wi-Fi Based Indoorpositioning

Fuzzy decision trees embedded with evolutionary fuzzy clustering for locating users using wireless signal strength in an indoor environment

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