Machine-Learning-Based Positioning: A Survey and Future Directions

Xu, Ke; Wang, Haiyang; Zhao, Yi; Wang, Xiaoliang; Shen, Meng

doi:10.1109/mnet.2019.1800366

Cited by 59 publications

(26 citation statements)

References 13 publications

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“…Typically, fingerprinting localization can be divided into two categories: machine learning [10,[34][35][36][37] and machine learning-free [40,[44][45][46][47][48][49][50][51][52][53]. Both techniques consist of two stages: offline training and online tagging.…”

Section: Fingerprint-based Methodsmentioning

confidence: 99%

“…Typically, Wi-Fi positioning techniques can be coarsely divided into two categories: fingerprinting-based [14][15][16][17][18][19] and ranging-based [20][21][22][23][24][25][26][27][28][29][30][31][32][33]. The former can be implemented with machine learning methods [10,[34][35][36][37] and machine learning-free methods [10,38]. This study investigates the Weighted K Nearest Neighboring (WKNN), which is a representative approach of the machine learning-free fingerprinting techniques since it can achieve an acceptable localization accuracy with a low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

Luo

et al. 2020

IJGI

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Wi-Fi fingerprinting has been widely used for indoor localization because of its good cost-effectiveness. However, it suffers from relatively low localization accuracy and robustness owing to the signal fluctuations. Virtual Access Points (VAP) can effectively reduce the impact of signal fluctuation problem in Wi-Fi fingerprinting. Current techniques normally use the Log-Normal Shadowing Model to estimate the virtual location of the access point. This would lead to inaccurate location estimation due to the signal attenuation factor in the model, which is difficult to be determined. To overcome this challenge, in this study, we propose a novel approach to calculating the virtual location of the access points by using the Apollonius Circle theory, specifically the distance ratio, which can eliminate the attenuation parameter term in the original model. This is based on the assumption that neighboring locations share the same attenuation parameter corresponding to the signal attenuation caused by obstacles. We evaluated the proposed method in a laboratory building with three different kinds of scenes and 1194 test points in total. The experimental results show that the proposed approach can improve the accuracy and robustness of the Wi-Fi fingerprinting techniques and achieve state-of-art performance.

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Section: Fingerprint-based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

Luo

et al. 2020

IJGI

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“…Some IPS-related reviews have over 200 references [16,17,18,19,20,21] or even over 300 references [22]. The insights presented in the rest of this paper are mainly based on 62 IPS-related survey works [6,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,…”

Section: An Overview Of Ips Solutionsmentioning

confidence: 99%

A Meta-Review of Indoor Positioning Systems

Mendoza-Silva

Torres-Sospedra

Huerta

2019

Sensors

203

130

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An accurate and reliable Indoor Positioning System (IPS) applicable to most indoor scenarios has been sought for many years. The number of technologies, techniques, and approaches in general used in IPS proposals is remarkable. Such diversity, coupled with the lack of strict and verifiable evaluations, leads to difficulties for appreciating the true value of most proposals. This paper provides a meta-review that performed a comprehensive compilation of 62 survey papers in the area of indoor positioning. The paper provides the reader with an introduction to IPS and the different technologies, techniques, and some methods commonly employed. The introduction is supported by consensus found in the selected surveys and referenced using them. Thus, the meta-review allows the reader to inspect the IPS current state at a glance and serve as a guide for the reader to easily find further details on each technology used in IPS. The analyses of the meta-review contributed with insights on the abundance and academic significance of published IPS proposals using the criterion of the number of citations. Moreover, 75 works are identified as relevant works in the research topic from a selection of about 4000 works cited in the analyzed surveys.

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“…• Although the exploration of ML with crowdsourcing has advanced significantly in the recent years, there are still some basic issues that remain to be studied [92]. • Potential directions exist to of positioning innovation by coordinating heterogeneous LBS frameworks and consistently indoor and outdoor situations [93]. There remain numerous challenges that can be explored in the future.…”

Section: Future Research Directionsmentioning

confidence: 99%

A Review of Machine Learning Algorithms for Cloud Computing Security

et al. 2020

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Cloud computing (CC) is on-demand accessibility of network resources, especially datastorage and processing power, without special and direct management by the users. CC recentlyhas emerged as a set of public and private datacenters that offers the client a single platform acrossthe Internet. Edge computing is an evolving computing paradigm that brings computation andinformation storage nearer to the end-users to improve response times and spare transmissioncapacity. Mobile CC (MCC) uses distributed computing to convey applications to cell phones.However, CC and edge computing have security challenges, including vulnerability for clients andassociation acknowledgment, that delay the rapid adoption of computing models. Machine learning(ML) is the investigation of computer algorithms that improve naturally through experience. In thisreview paper, we present an analysis of CC security threats, issues, and solutions that utilizedone or several ML algorithms. We review different ML algorithms that are used to overcomethe cloud security issues including supervised, unsupervised, semi-supervised, and reinforcementlearning. Then, we compare the performance of each technique based on their features, advantages,and disadvantages. Moreover, we enlist future research directions to secure CC models.

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Machine-Learning-Based Positioning: A Survey and Future Directions

Cited by 59 publications

References 13 publications

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP

A Meta-Review of Indoor Positioning Systems

A Review of Machine Learning Algorithms for Cloud Computing Security

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