Indoor Intelligent Fingerprint-Based Localization: Principles, Approaches and Challenges

Zhu, Xiaoqiang; Qu, Wenyu; Qiu, Tie; Zhao, Laiping; Atiquzzaman, Mohammed; Wu, Dapeng

doi:10.1109/comst.2020.3014304

Cited by 141 publications

(49 citation statements)

References 131 publications

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“…In [55] and [56], the authors explored future opportunities for localization services for 5G and beyond-5G (or 6G) wireless communications systems, where their key technologies (including ML-based schemes), underlying challenges, and potential solutions were discussed. In [57] and [58], the authors presented a review report on different rangingbased indoor localization. They discussed different types of fingerprints, such as CSI, visible light, and Bluetooth, and other localization methods.…”

Section: A Existing Indoor Localization Surveysmentioning

confidence: 99%

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Choe²,

2021

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In the era of the Internet of Things (IoT) and Industry 4.0, the indoor usage of smart devices is expected to increase, thereby making their location information more important. Based on various practical issues related to large delays, high design cost, and limited performance, conventional localization techniques are not practical for indoor IoT applications. In recent years, many researchers have proposed a wide range of machine learning (ML)-based indoor localization approaches using Wi-Fi received signal strength indicator (RSSI) fingerprints. This survey attempts to provide a summarized investigation of MLbased Wi-Fi RSSI fingerprinting schemes, including data preprocessing, data augmentation, ML prediction models for indoor localization, and postprocessing in ML, and compare their performance. Any ML-based study is heavily reliant on datasets. Therefore, we dedicate a significant portion of this survey to the discussion of dataset collection and open-source datasets. To provide good direction for future research, we discuss the current challenges and potential solutions related to ML-based indoor localization systems.

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Section: A Existing Indoor Localization Surveysmentioning

confidence: 99%

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Choe²,

2021

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“…This research will also use Random Forest and C5.0 Decision Trees as discussed previously. The last three models chosen were part of the algorithms discussed in the survey of [18], but performance measurements in terms of real-life localization classification were not disclosed. Extreme learning machines and radial basis function neural networks, which are feedforward neural networks that have the characteristics of close network structure and rapid learning that eliminates the problem of excessive training time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Extreme learning machines and radial basis function neural networks, which are feedforward neural networks that have the characteristics of close network structure and rapid learning that eliminates the problem of excessive training time. And the last algorithm, Adaptive Boosting (AdaBoost), is another ensemble algorithm that combines [18] <No test results > Adaptive Boosting (AdaBoost) [18] <No test results > several weak classifiers with their outputs put into a voting mechanism in order to provide a simple and fast improvement to the base classification capabilities of the weak model. A brief summary of the algorithms to be explored is found in Table 1.…”

Section: Literature Reviewmentioning

confidence: 99%

Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines

Paderes

Figueroa

Feria

2021

Frontiers in Artificial Intelligence and Applications

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Efforts toward COVID-19 proximity tracking in closed environments focus on efficient proximity identification by combining it with indoor localization theory for location activity monitoring and proximity detection. But these are met with concerns based on existing considerations of the localization theory like costly infrastructure, multi-story support, and over-reliance on sensor networks. Semantic location identities (SLI), or location data stored with additional meaningful context, has become a feasible localizing factor especially in locations that have multiple spaces with different usage from each other. There is also a novel method of classification framework, called hierarchical classification, that leverages the hierarchical structure of the labels to reduce model complexity. The research aims to provide a solution to proximity analysis and location activity monitoring considering guidelines released in a Philippine context that addresses concerns of indoor localization and handling of geospatial data by implementing a hybrid hierarchical indoor semantic location identity classification that focuses on observable events within context-unique locations.

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“…Consequently, a number of technologies, such as infrared (IR), Bluetooth, and Wi-Fi, have been developed to address the challenge of indoor positioning. These technologies have become widely used for indoor localization and positioning in recent years [ 7 ]. The propagation path of radio signals can be line-of-sight (LOS) or non-line-of-sight (NLOS) in indoor environments [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Sound Source Localization Using a Convolutional Neural Network and Regression Model

Tan

Lin

Chang

et al. 2021

Sensors

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In this research, a novel sound source localization model is introduced that integrates a convolutional neural network with a regression model (CNN-R) to estimate the sound source angle and distance based on the acoustic characteristics of the interaural phase difference (IPD). The IPD features of the sound signal are firstly extracted from time-frequency domain by short-time Fourier transform (STFT). Then, the IPD features map is fed to the CNN-R model as an image for sound source localization. The Pyroomacoustics platform and the multichannel impulse response database (MIRD) are used to generate both simulated and real room impulse response (RIR) datasets. The experimental results show that an average accuracy of 98.96% and 98.31% are achieved by the proposed CNN-R for angle and distance estimations in the simulation scenario at SNR = 30 dB and RT60 = 0.16 s, respectively. Moreover, in the real environment, the average accuracies of the angle and distance estimations are 99.85% and 99.38% at SNR = 30 dB and RT60 = 0.16 s, respectively. The performance obtained in both scenarios is superior to that of existing models, indicating the potential of the proposed CNN-R model for real-life applications.

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Indoor Intelligent Fingerprint-Based Localization: Principles, Approaches and Challenges

Cited by 141 publications

References 131 publications

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Machine Learning Based Indoor Localization Using Wi-Fi RSSI Fingerprints: An Overview

Implementing Hierarchical Indoor Semantic Location Identity Classification: A Case Study for COVID-19 Proximity Tracking in the Philippines

Sound Source Localization Using a Convolutional Neural Network and Regression Model

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