Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

Kim, Yungeun; Chon, Yohan; Cha, Hojung

doi:10.1109/tsmcc.2010.2093516

Cited by 116 publications

(62 citation statements)

References 10 publications

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“…These works successfully reduce effort in generating the radio maps provided that accurate indoor floor plans are given. Kim [25] proposes an autonomous fingerprinting method but requires the strong assumption that the initial location and direction of the user is known a priori. Recently, Walkie-Markie [11] proposes an algorithm to map pathways using WiFi-Marks.…”

Section: B Device-based Indoor Localizationmentioning

confidence: 99%

PiLoc: A self-calibrating participatory indoor localization system

Luo

Hong

Chan

2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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Abstract-While location is one of the most important context information in mobile and ubiquitous computing, large-scale deployment of indoor localization system remains elusive.In this work, we propose PiLoc, an indoor localization system that utilizes opportunistically sensed data contributed by users. Our system does not require manual calibration, prior knowledge and infrastructure support. The key novelty of PiLoc is that it merges walking segments annotated with displacement and signal strength information from users to derive a map of walking paths annotated with radio signal strengths.We evaluate PiLoc over 4 different indoor areas. Evaluation shows that our system can achieve an average localization error of 1.5m.

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Section: B Device-based Indoor Localizationmentioning

confidence: 99%

PiLoc: A self-calibrating participatory indoor localization system

Luo

Hong

Chan

2014

IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks

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“…First, the initial heading offset between user heading and device forward heading is zero, since users need to hold device in hand and gaze at it when they start the localization application. Second, the initial user heading and related device attitude are assumed to be known a priori [21,22], which can be obtained by Global Position System (GPS) tracking when the user enters a building, landmarks, or WiFi localization.…”

Section: User Heading Estimationmentioning

confidence: 99%

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Deng

Liu

et al. 2018

Wireless Communications and Mobile Computing

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Heading estimation using inertial sensors built-in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA-based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

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“…However, due to the complexity of infrastructure requirements, no distinguished solutions have been developed for indoor environments. One of the conventional mechanisms, the Received Signal Strength (RSS) coupled with Wi-Fi technology [14] can be used for radio fingerprinting. A set of RSS from a nearby access point, is collected and stored with corresponding location information in a radio map.…”

Section: Related Workmentioning

confidence: 99%

Effective Integration of Mobile Applications in the Context of Education System

PriyaBalasegaran¹,

Jayakumar²

2013

IJAIS

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With the advancement of wireless communication and portable devices, mobile users not only access information ubiquitously, but also use mobile devices for business transactions. Users exchange location, preferences and purchase details for discounts and privileges, in commercial applications. Mobile applications for educational purposes have plausible prospects. Applications for an educational gaming purpose, a classroom management control, a reference tool and an online study group can be developed. Massive Open Online Courses (MOOCs) aim to bring education to the entire world online, with access to university level courses, thereby promising a quality educational experience. This paper presents the concept applied in M-Commerce transactions, wherein a real time mobile application for an educational portal is developed, to enable e-learning in a wireless environment. It utilizes an Android platform for learners to access and refer online study materials, test themselves through mobile devices, and view their evaluation results. Consequently, intra and interdepartmental group analyses are generated, for assessment by academic heads. It also provides an opportunity to improve students' performance, for enlightening themselves intellectually and for sharing the knowledge gained among peers. This proposed model could be implemented as a MOOC across groups of institutions, interactive study forums and networking communities.

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Smartphone-Based Collaborative and Autonomous Radio Fingerprinting

Cited by 116 publications

References 10 publications

PiLoc: A self-calibrating participatory indoor localization system

PiLoc: A self-calibrating participatory indoor localization system

Robust Heading Estimation for Indoor Pedestrian Navigation Using Unconstrained Smartphones

Effective Integration of Mobile Applications in the Context of Education System

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