HybLoc: Hybrid Indoor Wi-Fi Localization Using Soft Clustering-Based Random Decision Forest Ensembles

Akram, Beenish Ayesha; Akbar, Ali Hammad; Shafiq, Omair

doi:10.1109/access.2018.2852658

Cited by 49 publications

(35 citation statements)

References 36 publications

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“…Carrying out a literature review, it was found that the only study to perform both localization and positioning procedures was conducted by Akram et.al. Also, according to the findings, it was seen that the proposed method provides higher performance values [14].…”

Section: Comparison Of Findings With Other Studiesmentioning

confidence: 63%

“…Until today, many positioning systems based on the changes in the RSSI indicator obtained from Wi-Fi or Bluetooth signals have been developed [10][11][12][13][14]. Moreover, initiatives inspired by these studies such as Insiteo [15], Wifarer [16], Insoft [17], The Framework for Internal Navigation and Discovery (FIND) [18], and MapsIndoors [19] were launched as well.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Seçkin

Coşkun

2019

Applied Sciences

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Wi-Fi-based indoor positioning offers significant opportunities for numerous applications. Examining the Wi-Fi positioning systems, it was observed that hundreds of variables were used even when variable reduction was applied. This reveals a structure that is difficult to repeat and is far from producing a common solution for real-life applications. It aims to create a common and standardized dataset for indoor positioning and localization and present a system that can perform estimations using this dataset. To that end, machine learning (ML) methods are compared and the results of successful methods with hierarchical inclusion are then investigated. Further, new features are generated according to the measurement point obtained from the dataset. Subsequently, learning models are selected according to the performance metrics for the estimation of location and position. These learning models are then fused hierarchically using deductive reasoning. Using the proposed method, estimation of location and position has proved to be more successful by using fewer variables than the current studies. This paper, thus, identifies a lack of applicability present in the research community and solves it using the proposed method. It suggests that the proposed method results in a significant improvement for the estimation of floor and longitude.

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Section: Comparison Of Findings With Other Studiesmentioning

confidence: 63%

Section: Introductionmentioning

confidence: 99%

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Seçkin

Coşkun

2019

Applied Sciences

View full text Add to dashboard Cite

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“…Xu et al [23] treated the extracted features as inputs to SVR and established the mapping between localization features and physical locations. Akram et al [24] proposed the hybrid indoor localization based on the use of random decision forest to achieve the room-level and latitude-longitude prediction. Gu et al [25] proposed a semisupervised deep extreme learning machine, which took advantage of deep learning and extreme learning machine methods and improved the accuracy and efficiency.…”

Section: Related Studiesmentioning

confidence: 99%

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Wang

Fan

et al. 2020

Mobile Information Systems

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Along with the advancement of wireless technology, indoor localization technology based on Wi-Fi has received considerable attention from academia and industry. The fingerprint-based method is the mainstream approach for Wi-Fi indoor localization and can be easily implemented without additional hardware. However, signal fluctuations constitute a critical issue pertaining to the extraction of robust features to achieve the required localization performance. This study presents a fingerprint feature extraction method commonly referred to as the Fisher score–stacked sparse autoencoder (Fisher–SSAE) method. Some features with low Fisher scores were eliminated, and the representative features were then extracted by the SSAE. Furthermore, this study establishes a hybrid localization model constructed with the use of the global model and the submodel to avoid significant coordinate localization errors attributed to subregional localization errors. Combined with three accessible fingerprint-based positioning methods, namely, the support vector regression, random forest regression, and the multiplayer perceptron classification, the experimental results demonstrate that the proposed methods improve the localization accuracy and response time compared to other feature extraction methods and the single localization model. Compared with some state-of-the-art methods, the proposed methods have better localization performances when large number of features are used.

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“…In Ref. [ 36 ], an indoor localization system using WLAN named HybLoc was proposed. The system utilized Gaussian mixture model-based soft clustering and random decision forest for localization.…”

Section: Related Workmentioning

confidence: 99%

Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN

Sun

Meng

et al. 2018

Sensors

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In the last decade, fingerprinting localization using wireless local area network (WLAN) has been paid lots of attention. However, this method needs to establish a database called radio map in the off-line stage, which is a labor-intensive and time-consuming process. To save the radio map establishment cost and improve localization performance, in this paper, we first propose a Voronoi diagram and crowdsourcing-based radio map interpolation method. The interpolation method optimizes propagation model parameters for each Voronoi cell using the received signal strength (RSS) and location coordinates of crowdsourcing points and estimates the RSS samples of interpolation points with the optimized propagation model parameters to establish a new radio map. Then a general regression neural network (GRNN) is employed to fuse the new and original radio maps established through interpolation and manual operation, respectively, and also used as a fingerprinting localization algorithm to compute localization coordinates. The experimental results demonstrate that our proposed GRNN fingerprinting localization system with the fused radio map is able to considerably improve the localization performance.

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HybLoc: Hybrid Indoor Wi-Fi Localization Using Soft Clustering-Based Random Decision Forest Ensembles

Cited by 49 publications

References 36 publications

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

Hierarchical Fusion of Machine Learning Algorithms in Indoor Positioning and Localization

A Hybrid Wi-Fi Fingerprint-Based Localization Scheme Achieved by Combining Fisher Score and Stacked Sparse Autoencoder Algorithms

Voronoi Diagram and Crowdsourcing-Based Radio Map Interpolation for GRNN Fingerprinting Localization Using WLAN

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