An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

Zhao, Jianli; Gao, Xiang; Wang, Xin; Li, Chun-Xiu; Song, Min; Sun, Qiuxia

doi:10.1017/s037346331800019x

Cited by 13 publications

(12 citation statements)

References 28 publications

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“…With the vigorous development of machine learning, more and more algorithms are playing their role in big data processing [4][5][6]. Among them, the recommendation system has achieved great success in information filtering [7][8][9] and tag recommendation can be used to describe the user's preference and item's characteristics more efficiently.…”

Section: Related Workmentioning

confidence: 99%

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

Zhao¹,

Zhang²,

Sun³

et al. 2021

KSII TIIS

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The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance.

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Section: Related Workmentioning

confidence: 99%

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

Zhao¹,

Zhang²,

Sun³

et al. 2021

KSII TIIS

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show abstract

“…ML has been used in fingerprinting solutions since its infancy, where standard ML methods have been utilized as matching mechanism, e.g., [6], [315]. Since then it has been utilized in other aspects as well, for instance for feature extraction and radio-map construction [72], [72], [91], [94], [103], [138], [142], [154], [231], [297], radio-map updating [85], [122], [132], [181], hierarchical solutions [75], [151], [222], [256], [316], and robust matching [79], [86], [88]. This is expected because fingerprinting systems, similar to ML, are data-driven and both may 9 operate in a training (offline) phase, and an online phase.…”

Section: F Features Utilization In Conventional and Ml-based Localiza...mentioning

confidence: 99%

“…Standard ML [53], [54], [56], [57], [63], [68]- [72], [79]- [81], [83], [84], [86]- [89], [91], [92], [94], [96], [97], [102], [116]- [118], [120]- [122], [125], [126], [128], [129], [131], [132], [136]- [138], [140], [142], [181]- [184], [244], [249] [60], [61], [72], [75], [76], [104], [106], [108]- [110], [112], [156], [157], [160]- [162], …”

Section: Supervisedmentioning

confidence: 99%

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A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

Burghal,

Ravi,

Rao

et al. 2020

Preprint

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The last few decades have witnessed a growing interest in location-based services. Using localization systems based on Radio Frequency (RF) signals has proven its efficacy for both indoor and outdoor applications. However, challenges remain with respect to both complexity and accuracy of such systems. Machine Learning (ML) is one of the most promising methods for mitigating these problems, as ML (especially deep learning) offers powerful practical data-driven tools that can be integrated into localization systems. In this paper, we provide a comprehensive survey of ML-based localization solutions that use RF signals. The survey spans different aspects, ranging from the system architectures, to the input features, the ML methods, and the datasets.A main point of the paper is the interaction between the domain knowledge arising from the physics of localization systems, and the various ML approaches. Besides the ML methods, the utilized input features play a major role in shaping the localization solution; we present a detailed discussion of the different features and what could influence them, be it the underlying wireless technology or standards or the preprocessing techniques. A detailed discussion is dedicated to the different ML methods that have been applied to localization problems, discussing the underlying problem and the solution structure. Furthermore, we summarize the different ways the datasets were acquired, and then list the publicly available ones. Overall, the survey categorizes and partly summarizes insights from almost 400 papers in this field.This survey is self-contained, as we provide a concise review of the main ML and wireless propagation concepts, which shall help the researchers in either field navigate through the surveyed solutions, and suggested open problems.

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“…However, when the fingerprints are sparse, these methods can not capture the random properties such as signal propagation characteristic, which leads to low localization accuracy. Other regression-based methods, such as Gaussian process regression and support vector regression [8,9] estimate the expected RSS at non-sitesurveyed locations to reduce the site survey effort.…”

Section: Related Workmentioning

confidence: 99%

CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

et al. 2020

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An Efficient Radio Map Updating Algorithm based on K-Means and Gaussian Process Regression

Cited by 13 publications

References 28 publications

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

FolkRank++: An Optimization of FolkRank Tag Recommendation Algorithm Integrating User and Item Information

A Comprehensive Survey of Machine Learning Based Localization with Wireless Signals

CSELM‐QE: A Composite Semi‐supervised Extreme Learning Machine with Unlabeled RSS Quality Estimation for Radio Map Construction

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