Improving Positioning Accuracy Using WLAN Optimization for Location Based Services and Cognitive Radio Networks

Khattak, Sohaib Bin Altaf; Jia, Min; Guo, Qing; Gu, Xuemai

doi:10.1007/978-3-030-93398-2_54

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…The experimental results of the proposed BoF indoor positioning model are validated through both simulated virtual and real-time testbeds. We choose two simulation scenarios from our previous work [ 41 , 42 ], and a personal testbed was set up consisting of two apartments in a residential building. All approaches are run on MATLABR2018a installed on a Dell laptop equipped with an Intel Core i7 processor and 8 GB RAM.…”

Section: Performance Evaluationmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

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Location-based services have permeated Smart academic institutions, enhancing the quality of higher education. Position information of people and objects can predict different potential requirements and provide relevant services to meet those needs. Indoor positioning system (IPS) research has attained robust location-based services in complex indoor structures. Unforeseeable propagation loss in complex indoor environments results in poor localization accuracy of the system. Various IPSs have been developed based on fingerprinting to precisely locate an object even in the presence of indoor artifacts such as multipath and unpredictable radio propagation losses. However, such methods are deleteriously affected by the vulnerability of fingerprint matching frameworks. In this paper, we propose a novel machine learning framework consisting of Bag-of-Features and followed by a k-nearest neighbor classifier to categorize the final features into their respective geographical coordinate data. BoF calculates the vocabulary set using k-mean clustering, where the frequency of the vocabulary in the raw fingerprint data represents the robust final features that improve localization accuracy. Experimental results from simulation-based indoor scenarios and real-time experiments demonstrate that the proposed framework outperforms previously developed models.

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Section: Performance Evaluationmentioning

confidence: 99%

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

Khattak

Fawad

Nasralla

et al. 2022

Sensors

Self Cite

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“…Therefore, acquiring a predicted indoor path loss map (IPM) or received signal strength (RSS) map prior to the deployment of APs is essential as it allows for accurate estimation of signal strength and coverage within buildings and helps in the placement of APs. In addition, an accurate IPM can enable applications such as precise indoor localisation [2], cognitive radio networks [3], and mobile robots [4].…”

Section: Introductionmentioning

confidence: 99%

IRDM: A Generative Diffusion Model for Indoor Radio Map Interpolation

Qiu,

Bakirtzis,

Wassell

et al. 2023

GLOBECOM 2023 - 2023 IEEE Global Communications Conference

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This article proposes a novel methodology for interpolating path-loss radio maps, which are vital for comprehending signal distribution and hence planning indoor wireless networks. The approach employs generative diffusion models and proves to be highly effective in generating accurate radio maps with only a small number of measurements. The experimental outcomes demonstrate an average root-mean-square error of 4.23 dB using only 10 percent of the reference points, highlighting the ability of the generative diffusion model to achieve significant interpolation accuracy in radio map generation.

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Improving Positioning Accuracy Using WLAN Optimization for Location Based Services and Cognitive Radio Networks

Cited by 2 publications

References 11 publications

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

WLAN RSS-Based Fingerprinting for Indoor Localization: A Machine Learning Inspired Bag-of-Features Approach

IRDM: A Generative Diffusion Model for Indoor Radio Map Interpolation

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