Miskolc IIS hybrid IPS: Dataset for hybrid indoor positioning

Tóth, Zsolt; Tamás, Judit

doi:10.1109/radioelek.2016.7477348

Cited by 42 publications

(18 citation statements)

References 15 publications

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“…Miskolc IIS (Institute of Information Science) Hybrid IPS (Indoor Positioning System) data set [ 40 , 41 ] was recorded by the ILONA (Indoor Localization and Navigation) System [ 42 ] in the IIS Building at University of Miskolc. [ 43 ] The measurement environment is a grid-like layout of the three-story building, as seen in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Yuan

Zha

Zhang

2020

Sensors

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The complex indoor environment makes the use of received fingerprints unreliable as an indoor positioning and localization method based on fingerprint data. This paper proposes an adaptive multi-type fingerprint indoor positioning and localization method based on multi-task learning (MTL) and Weight Coefficients K-Nearest Neighbor (WCKNN), which integrates magnetic field, Wi-Fi and Bluetooth fingerprints for positioning and localization. The MTL fuses the features of different types of fingerprints to search the potential relationship between them. It also exploits the synergy between the tasks, which can boost up positioning and localization performance. Then the WCKNN predicts another position of the fingerprints in a certain class determined by the obtained location. The final position is obtained by fusing the predicted positions using a weighted average method whose weights are the positioning errors provided by positioning error prediction models. Experimental results indicated that the proposed method achieved 98.58% accuracy in classifying locations with a mean positioning error of 1.95 m.

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Section: Methodsmentioning

confidence: 99%

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Yuan

Zha

Zhang

2020

Sensors

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“…The dataset aims at providing the data from several technologies to perform hybrid indoor positioning [53]. For this purpose, the data are collected for the Wi-Fi, RFID, Bluetooth, and magnetic field using Samsung Galaxy Young GT-S5360 with Android 4.4.4.…”

Section: Miskolc Iis Hybrid Ipsmentioning

confidence: 99%

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning

Ashraf

Din

Hur

et al. 2021

Sensors

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Indoor positioning and localization have been regarded as some of the most widely researched areas during the last decade. The wide proliferation of smartphones and the availability of fast-speed internet have initiated several location-based services. Concerning the importance of precise location information, many sensors are embedded into modern smartphones. Besides Wi-Fi positioning, a rich variety of technologies have been introduced or adopted for indoor positioning such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, etc. However, special emphasis is put on infrastructureless approaches like Wi-Fi and magnetic field-based positioning, as they do not require additional infrastructure. Magnetic field positioning is an attractive solution for indoors; yet lack of public benchmarks and selection of suitable benchmarks are among the big challenges. While several benchmarks have been introduced over time, the selection criteria of a benchmark are not properly defined, which leads to positioning results that lack generalization. This study aims at analyzing various public benchmarks for magnetic field positioning and highlights their pros and cons for evaluation positioning algorithms. The concept of DUST (device, user, space, time) and DOWTS (dynamicity, orientation, walk, trajectory, and sensor fusion) is introduced which divides the characteristics of the magnetic field dataset into basic and advanced groups and discusses the publicly available datasets accordingly.

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“…There are many public IPS dataset that can be obtained online such as [8] and [9]. However, these datasets were collected in one day only and do not take into account the changes in the temporal signal that inevitably occur in the real environment.…”

Section: Introductionmentioning

confidence: 99%

Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

Thenuardi¹,

Soewito²

2020

Adv. sci. technol. eng. syst. j.

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Indoor positioning system (IPS) has become a high demand research field to be developed and has made considerable progress in recent years. Wi-Fi fingerprinting is the most promising technique that produces an acceptable result. However, despite the large amount of research that has been done using Wi-Fi fingerprinting, only a few Wi-Fi based IPS in the market can be said to be successful. Doing the research in a controlled environment and ignore the temporal signal changes may be the cause of such scenario. A long-term dataset was built to overcome this issue, yet the distance error of the state of the art was 2.48m. Therefore, we aim to reduce the distance error by combining two positioning algorithms which are Weighted k-Nearest Neighbor (WKNN) and Long-Short Term Memory (LSTM) using ensemble learning. The result shows that our ensemble method can reduce the localization error to 1.89m and improve the performance of the IPS by 23.7% when compared to the state of the art.

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Miskolc IIS hybrid IPS: Dataset for hybrid indoor positioning

Cited by 42 publications

References 15 publications

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Adaptive Multi-Type Fingerprint Indoor Positioning and Localization Method Based on Multi-Task Learning and Weight Coefficients K-Nearest Neighbor

Empirical Overview of Benchmark Datasets for Geomagnetic Field-Based Indoor Positioning

Indoor Positioning System using WKNN and LSTM Combined via Ensemble Learning

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