Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Leu, Jenq‐Shiou; Yu, M.; Tzeng, Hung-Jie

doi:10.1016/j.comnet.2015.08.032

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…A participatory sensing approach can also be used to train the classification model [47], thus alleviating bootstrapping issues in such methods. Quite often, indoor localisation uses Received Signal Strength (RSS) as a metric for location fingerprinting [15,20,46,50]. RSS fingerprints are then mapped to relative spatial coordinates which are geographically connected to user movement paths.…”

Section: Localisation 21mentioning

confidence: 99%

Environmental exposure assessment using indoor/outdoor detection on smartphones

Anagnostopoulos

García

Gonçalves

et al. 2017

Pers Ubiquit Comput

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We present an energy-efficient method for Indoor/Outdoor detection on smartphones. The creation of an accurate environmental exposure detection method enables crucial advances to a number of health sciences, which seek to model patients' environmental exposure. In a field trial, we collected data from multiple smartphone sensors, along with explicit indoor/outdoor labels entered by participants. Using this rich dataset, we evaluate multiple classification models, optimized for accuracy and low energy consumption. Using all sensors, we can achieve 99% classification accuracy. Using only a subset of energy-efficient sensors we achieve 92.91% accuracy. We systematically quantify how subsampling can be used as a trade-off for accuracy and energy consumption. Our work enables researchers to quantify environmental exposure using commodity smartphones.

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Section: Localisation 21mentioning

confidence: 99%

Environmental exposure assessment using indoor/outdoor detection on smartphones

Anagnostopoulos

García

Gonçalves

et al. 2017

Pers Ubiquit Comput

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“…In the work of W. Xiao et al [7], a stochastic system model is adopted to track the target’s position via an inertial measurement unit integrated with the WiFi tag. To overcome the severe signal instability problem in the indoor environment, in the work of Jenq-Shiou Leu et al [16], an RSS fingerprint and footprint matching mechanism with the assistance of collecting ambient WiFi RSSs from not only the intrinsic APs but also the extrinsic APs is proposed. All of the WiFi fingerprint-based approaches achieved a better positioning performance, but the fingerprint acquisition work is needed in advance.…”

Section: Related Workmentioning

confidence: 99%

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

Wang

Jiang

Guo

et al. 2016

Sensors

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Recently location based services (LBS) have become increasingly popular in indoor environments. Among these indoor positioning techniques providing LBS, a fusion approach combining WiFi-based and pedestrian dead reckoning (PDR) techniques is drawing more and more attention of researchers. Although this fusion method performs well in some cases, it still has some limitations, such as heavy computation and inconvenience for real-time use. In this work, we study map information of a given indoor environment, analyze variations of WiFi received signal strength (RSS), define several kinds of indoor landmarks, and then utilize these landmarks to correct accumulated errors derived from PDR. This fusion scheme, called Landmark-aided PDR (LaP), is proved to be light-weight and suitable for real-time implementation by running an Android application designed for the experiment. We compared LaP with other PDR-based fusion approaches. Experimental results show that the proposed scheme can achieve a significant improvement with an average accuracy of 2.17 m.

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“…Apart from the advantages, location recognition using the fingerprint method of course also has limitations. For example, the limitations of signal strength received by the device heavily depend on the existing environmental model, the used chipset, the antenna on the smartphone and the position of the user's smartphone itself [4,11].…”

Section: Introductionmentioning

confidence: 99%

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

Dari

Suyoto

Pranowo

2018

Int. J. Interact. Mob. Technol.

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Abstract-The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS's fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

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Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Cited by 15 publications

References 6 publications

Environmental exposure assessment using indoor/outdoor detection on smartphones

Environmental exposure assessment using indoor/outdoor detection on smartphones

An Indoor Positioning Method for Smartphones Using Landmarks and PDR

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

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