GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Zhang, Chi; Subbu, Kalyan Pathapati; Luo, Jun; Wu, Jianxin

doi:10.1109/tmc.2014.2319824

Cited by 116 publications

(61 citation statements)

References 35 publications

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“…It relies on geomagnetic fingerprints that are far more stable than Wi-Fi fingerprints, and it exploits crowd sensing to construct floor maps than expecting individual venues to supply digitized maps [12]. Based on their experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.…”

Section: F Groping: Geomagnetism Andmentioning

confidence: 99%

An Indoor Navigation System Using IOT

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2018

IJRASET

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Big shopping malls usually provide a directory to their available shops, but these directories are most of the time static and do not provide any interactivity features to the visitors. In this work, we present a mobile shopping mall navigator. The main reason behind our conceptual idea of our proposed project is because we feel that when visitors often change their plan to go to other shops instead of the ones in their minds, it can be full of effort especially considering the crowded levels and location of the navigation material. The application developed is practical and feasible.Smart-Phones have become very popular these days, so we have combined the idea. Smart-Phone application helping you in an alienated mall. The idea revolves around our SmartPhones & the "Wi-Fi" provided by the mall. An application that needsreal-time, fast, & reliable data processing. Technical Keywords-Indoor navigation, QR-Code scanner, Wi-Fi router I. INTRODUCTIONManual Shopping is the traditional way of shopping where the customers choose their desired productand carry the products along with them. Traditional shopping is a tedious and time consuming job. Intraditional shopping, the customer has to wait in long queues at the cash counter. This consumeslot of time and energy of both the customer as well as cashier. To overcome these flaws, the customerhimself can scan the QR-Code using his mobile while making purchase, retrieve essential details of allproducts from shops database and generate bill himself [11]. This bill can be sent to the customer's mobilethrough online banking service thus the user can make quick payment and leave the shop early.TheQR-Code of the product is scanned by the customer and move to the wish list if they are interested inchoice of item by using the proposed mobile application. In order to developan Android Applicationthat uses a QR-Code scanner for the purchasing and navigation[4] of items for store thatwill be self-checking and automatic payment transaction [1]. Here comes the term indoor navigation [5] andQR-Code scanning.Indoor positioning is still a challenging problem because satellite-based approach does not work properly inside buildings [1]. QR-Codes are ubiquitously used to identify products,goods or deliveries. Devices to read QR-Codesare allaround,in the form of pen type readers, laser scanners or LED scanners. Camera-based readers,as a new kind of QR-Code reader, have recently gained much attention. The interest in camera-basedQR-Code recognition is built on the fact that numerous mobile devices are already in use, which provide the capability to take images of a fair quality [11].This describes the hardware system architecturefor implementing the QR-Code reading system in mobile phones and its process. The camera device andapplication processors are necessary hardware components for the system. The application processorsare needed to implement the camera interface, LCD controllers, DSP for image processing, and application host in CPU for real-time computations. The application p...

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Section: F Groping: Geomagnetism Andmentioning

confidence: 99%

An Indoor Navigation System Using IOT

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2018

IJRASET

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“…Specifically, a number of studies combined Wi-Fi RSS fingerprinting and floor plans established through site surveys to build IPSs. Recent studies such as LiFS, 32 Zee, 33 Mole´, 34 and GROPING 35 introduced IPSs that overcome the shortcomings of time-consuming and labor-intensive site surveys through crowdsourcing using sensors embedded in smartphones such as accelerometers, gyroscopes, and compasses. In addition, some studies have analyzed customer movement paths or performed customer behavior analysis based on collected sensor data.…”

Section: Customer Behavior Analysis and Store Layout Optimizationmentioning

confidence: 99%

Store layout optimization using indoor positioning system

Hwangbo

Kim

Lee

et al. 2017

International Journal of Distributed Sensor Networks

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Indoor positioning systems have attracted considerable attention from practitioners and firms seeking to optimize the consumer shopping experience with the goal of attaining increased revenue and profitability. Acknowledging the importance of indoor positioning systems in store layout optimization, we conducted a field experiment for 11 months in order to develop algorithms for connecting indoor positioning data with customer transaction data. Using fingerprinting as a primary data collection technique, we compared positioning and transaction data before and after critical store layout optimization decisions in order to identify which customer movement patterns generated the highest sales. In contrast to previous works on indoor positioning systems, which focused solely on developing algorithms or techniques to increase accuracy rates, our algorithms in principle integrate computing and marketing perspectives. Our findings can be applied to store layout optimization and personalized marketing.

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“…Ficco et al [8] propose to optimize the positioning accuracy by selecting the best deployment schema of the wireless access points. There are other types of fingerprints or landmarks used to achieve room-level localization, e.g., [13], [21], [35]. Batphone [21] uses an ambient sound fingerprint called the Acoustic Background Spectrum (ABS), and GROP-ING [35] uses Geo-magnetism as fingerprint.…”

Section: Related Workmentioning

confidence: 99%

“…There are other types of fingerprints or landmarks used to achieve room-level localization, e.g., [13], [21], [35]. Batphone [21] uses an ambient sound fingerprint called the Acoustic Background Spectrum (ABS), and GROP-ING [35] uses Geo-magnetism as fingerprint. Luxapose [13] encodes location identifiers in visible light and a camera-equipped smartphone can determine its location and orientation relative to the luminaires.…”

Section: Related Workmentioning

confidence: 99%

Montage: Combine Frames with Movement Continuity for Realtime Multi-User Tracking

Zhang

Liu

Jiang

et al. 2017

IEEE Trans. on Mobile Comput.

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In this work we design and develop Montage for real-time multi-user formation tracking and localization by off-the-shelf smartphones. Montage achieves submeter-level tracking accuracy by integrating temporal and spatial constraints from user movement vector estimation and distance measuring. In Montage we designed a suite of novel techniques to surmount a variety of challenges in real-time tracking, without infrastructure and fingerprints, and without any a priori user-specific (e.g., stride-length and phone-placement) or site-specific (e.g., digitalized map) knowledge: (1) a coded audio tone to support multi-user tracking with minimal latency, in the presence of high noise, multi-path effect and Doppler Shift (2) an innovative stride-length and walking direction estimation method without a priori knowledge of user and site, (3) a vector-based multi-user tracking scheme which connects successive localization snapshots to refine users' locations and generate continuous moving traces. We implemented, deployed and evaluated Montage in both outdoor and indoor environment. Our experimental results (847 traces from 15 users) show that the stride-length estimated by Montage over all users has error within 9cm, and the moving-direction estimated by Montage is within 20 o . For real-time tracking, Montage provides meter-second-level formation tracking accuracy with off-the-shelf mobile phones.

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GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Cited by 116 publications

References 35 publications

An Indoor Navigation System Using IOT

An Indoor Navigation System Using IOT

Store layout optimization using indoor positioning system

Montage: Combine Frames with Movement Continuity for Realtime Multi-User Tracking

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