A Robust Zero-Calibration RF-Based Localization System for Realistic Environments

Elbakly, Rizanne; Youssef, Moustafa

doi:10.1109/sahcn.2016.7732967

Cited by 61 publications

(48 citation statements)

References 39 publications

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“…Then, we evaluate its 2D location accuracy. In addition, we compare its accuracy to two state-of-theart calibration-free systems: Locus [18] and IncVoronoi [22].…”

Section: Discussionmentioning

confidence: 99%

“…Then, we show Hapi's performance in the 5 testbeds as compared to Locus [18]. Note that, we do not compare to IncVoronoi [22] here as it assumes a single-floor area of interest and estimates the user 2D location only. Figure 12 shows the effect of the profile window size N f on Hapi's exact floor detection accuracy.…”

Section: Floor-level Detection Accuracymentioning

confidence: 99%

“…This is better than Locus [18] by 52.1% and 47.7% respectively. In addition, Hapi identifies the user's 2D location on that floor with a median accuracy of 3.5m overall testbeds, achieving an improvement of 60.7% and 76.0% over IncVoronoi [22] and Locus [18] respectively. We believe Hapi's performance, over such large scale testbeds, marks a significant milestone towards achieving a true ubiquitous indoor localization system.…”

Section: Introductionmentioning

confidence: 99%

“…Another major additional limitation for the vast majority of available systems, e.g. [7,22,26,32,38,43], is focusing on a single-floor area of interest which reduces the usefulness of their estimated location in realistic multistory buildings scenarios [18].…”

Section: Introductionmentioning

confidence: 99%

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Hapi

Aly

Agrawala

2018

Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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In this paper, we present Hapi, a novel system that uses off-theshelf standard WiFi to provide pseudo-3D indoor localization-it estimates the user's floor and her 2D location on that floor. Hapi is calibration-free, only requiring the building's floorplans and its WiFi APs' installation location for deployment. Our analysis shows that while a user can hear APs from nearby floors as well as her floor, she will typically only receive signals from spatially closer APs in distant floors, as compared to APs in her floor. This is due to signal attenuation by floors/ceilings along with the 3D distance between the APs and the user. Hapi leverages this observation to achieve accurate and robust location estimates.A deep-learning based method is proposed to identify the user's floor. Then, the identified floor along with the user's visible APs from all floors are used to estimate her 2D location through a novel RSS-Rank Gaussian-based method. Additionally, we present a regression based method to predict Hapi's location estimates' quality and employ it within a Kalman Filter to further refine the accuracy. Our evaluation results, from deployment on various android devices over 6 months with 13 subjects in 5 different up to 9 floors multistory buildings, show that Hapi can identify the user's exact floor up to 95.2% of the time and her 2D location with a median accuracy of 3.5m, achieving 52.1% and 76.0% improvement over related calibration-free state-of-the-art systems respectively.

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“…Then, we evaluate its 2D location accuracy. In addition, we compare its accuracy to two state-of-theart calibration-free systems: Locus [18] and IncVoronoi [22].…”

Section: Discussionmentioning

confidence: 99%

Section: Floor-level Detection Accuracymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hapi

Aly

Agrawala

2018

Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services

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“…RadioGrapher can work with any devicebased location tracking technology, e.g. [10], [11]. Without loss of generality, we use the calibration-free IncVor system in this paper [11].…”

Section: Introductionmentioning

confidence: 99%

Zero-Calibration Device-Free Localization for the IoT Based on Participatory Sensing

Ibrahim

Gomaa²,

Youssef

2018

2018 IEEE Global Communications Conference (GLOBECOM)

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Device-free localization (DFL) is an emerging technology for estimating the position of a human or object that is not equipped with any electronic tag, nor participate actively in the localization process. Similar to device-based localization, the initial phase in DFL is to build the fingerprint database which is usually done manually using site surveying. This process is tedious, time-consuming, and vulnerable to environmental dynamics. Motivated by the recent advances in the Internet of Things (IoT), this paper introduces RadioGrapher; a system that automates the process of device-free fingerprint calibration in IoT environments. RadioGrapher leverages the device-based locations of entities in the area of interest in a crowd-sensing manner, aided with Fresnel zones of the wirelessly connected IoT devices to automatically construct a device-free fingerprint.Experimental evaluation of RadioGrapher in an IoT testbed using multiple entities shows that it can construct DFL fingerprints with high accuracy. Moreover, its median localization accuracy is comparable to that of manual fingerprinting. This comes with no calibration overhead, highlighting the promise of RadioGrapher as a crowdsourcing device-free fingerprint constructor in IoT environments.

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