BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Zheng, Zengwei; Chen, Yuanyi; Chen, Sinong; Sun, Lin; Chen, Dan

doi:10.1155/2016/1289013

Cited by 9 publications

(3 citation statements)

References 29 publications

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“…Experiment output shows promising results. Similarly, the authors of [35] introduced the use of Kullback–Leibler Divergence to calculate the RSS similarity with the Euclidean distance between two location fingerprints to reduce the RSS variance effect of different devices. The results show improved positioning accuracy with various phones.…”

Section: Related Workmentioning

confidence: 99%

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Ashraf

Hur

Park

2019

Sensors

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The applications of location-based services require precise location information of a user both indoors and outdoors. Global positioning system’s reduced accuracy for indoor environments necessitated the initiation of Indoor Positioning Systems (IPSs). However, the development of an IPS which can determine the user’s position with heterogeneous smartphones in the same fashion is a challenging problem. The performance of Wi-Fi fingerprinting-based IPSs is degraded by many factors including shadowing, absorption, and interference caused by obstacles, human mobility, and body loss. Moreover, the use of various smartphones and different orientations of the very same smartphone can limit its positioning accuracy as well. As Wi-Fi fingerprinting is based on Received Signal Strength (RSS) vector, it is prone to dynamic intrinsic limitations of radio propagation, including changes over time, and far away locations having similar RSS vector. This article presents a Wi-Fi fingerprinting approach that exploits Wi-Fi Access Points (APs) coverage area and does not utilize the RSS vector. Using the concepts of APs coverage area uniqueness and coverage area overlap, the proposed approach calculates the user’s current position with the help of APs’ intersection area. The experimental results demonstrate that the device dependency can be mitigated by making the fingerprinting database with the proposed approach. The experiments performed at a public place proves that positioning accuracy can also be increased because the proposed approach performs well in dynamic environments with human mobility. The impact of human body loss is studied as well.

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Section: Related Workmentioning

confidence: 99%

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Ashraf

Hur

Park

2019

Sensors

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“…Wi-Fi APs are grouped based on their RSSI to improve the positioning accuracy. Similarly, Kullback-Leibler divergence is used in [31] to calculate the RSS similarity, which improves the positioning performance. An approach known as weighted RSS (WRSS) is proposed in [32], which mitigates the influence of varying RSS and improves the accuracy.…”

Section: Time Related Mutation Of Radio Signalsmentioning

confidence: 99%

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Ashraf¹,

Din²,

Hur³

et al. 2022

Computers, Materials &Amp; Continua

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Precise information on indoor positioning provides a foundation for position-related customer services. Despite the emergence of several indoor positioning technologies such as ultrawideband, infrared, radio frequency identification, Bluetooth beacons, pedestrian dead reckoning, and magnetic field, Wi-Fi is one of the most widely used technologies. Predominantly, Wi-Fi fingerprinting is the most popular method and has been researched over the past two decades. Wi-Fi positioning faces three core problems: device heterogeneity, robustness to signal changes caused by human mobility, and device attitude, i.e., varying orientations. The existing methods do not cover these aspects owing to the unavailability of publicly available datasets. This study introduces a dataset that includes the Wi-Fi received signal strength (RSS) gathered using four different devices, namely Samsung Galaxy S8, S9, A8, LG G6, and LG G7, operated by three surveyors, including a female and two males. In addition, three orientations of the smartphones are used for the data collection and include multiple buildings with a multifloor environment. Various levels of human mobility have been considered in dynamic environments. To analyze the time-related impact on Wi-Fi RSS, data over 3 years have been considered.

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“…These algorithms do not meet our goal of not relying on extensive prior layout knowledge. Very recently, Zhen et al have presented BigLoc [13] which specifically attempts to solve the localisation problem in large indoor spaces using hundreds of access points for floor level localisation. Chen et al propose a method Graph Loc [8] which requires no upfront calibration.…”

Section: Related Workmentioning

confidence: 99%

Understanding the quality of calibrations for indoor localisation

McConville

Byrne

Craddock

et al. 2018

2018 IEEE 4th World Forum on Internet of Things (WF-IoT)

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The efficient and effective deployment of Internet of Things (IoT) systems in real world scenarios remains a challenge, particularly in applications such as indoor localisation. Various methods have been proposed recently to calibrate localisation systems, ranging from precise but time consuming processes to those involving little explicit calibration based on a crowdsourced collection of data over time. However it is not clear how to estimate and compare the quality of a specific instance of a calibration. In this paper we present a simple yet effective method of calibrating a Smart Home in a Box (SHiB) together with a framework to combine calibrations while assessing their quality. Our empirical results demonstrate that our calibration method can be performed by untrained users in a short period of time yet is capable of up to 92% accuracy in room level localisation on free living experimental data.

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BigLoc: A Two-Stage Positioning Method for Large Indoor Space

Cited by 9 publications

References 29 publications

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Indoor Positioning on Disparate Commercial Smartphones Using Wi-Fi Access Points Coverage Area

Wi-Fi Positioning Dataset with Multiusers and Multidevices Considering Spatio-Temporal Variations

Understanding the quality of calibrations for indoor localisation

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