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The Tale of Two Localization Technologies: Enabling Accurate Low-Overhead WiFi-based Localization for Low-end Phones

Published: 07 November 2017 Publication History

Abstract

WiFi fingerprinting is one of the mainstream technologies for indoor localization. However, it requires an initial calibration phase during which the fingerprint database is built manually by site surveyors. This process is labour intensive, tedious, and needs to be repeated with any change in the environment. While a number of recent systems have been introduced to reduce the calibration effort through RF propagation models and/or crowdsourcing, these still have some limitations. Other approaches use the recently developed iBeacon technology as an alternative to WiFi for indoor localization. However, these beacon-based solutions are limited to a small subset of high-end phones.
In this paper, we present HybridLoc: an accurate low-overhead indoor localization system. The basic idea HybridLoc builds on is to leverage the sensors of high-end phones to enable localization of lower-end phones. Specifically, the WiFi fingerprint is crowdsourced by opportunistically collecting WiFi-scans labeled with location data obtained from BLE-enabled high-end smart phones. These scans are used to automatically construct the WiFi-fingerprint, that is used later to localize any lower-end cell phone with the ubiquitous WiFi technology. HybridLoc also has provisions for handling the inherent error in the estimated BLE locations used in constructing the fingerprint as well as to handle practical deployment issues including the noisy wireless environment, heterogeneous devices, among others.
Evaluation of HybridLoc using Android phones shows that it can provide accurate localization in the same range as manual fingerprinting techniques under the same deployment conditions. Moreover, the localization accuracy on low-end phones supporting only WiFi is comparable to that achieved with high-end phones supporting BLE. This accuracy is achieved with no training overhead, is robust to the different user devices, and is consistent under environment changes.

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    cover image ACM Conferences
    SIGSPATIAL '17: Proceedings of the 25th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
    November 2017
    677 pages
    ISBN:9781450354905
    DOI:10.1145/3139958
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 07 November 2017

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    Author Tags

    1. BLE-based Localization
    2. Indoor Localization
    3. WiFi Fingerprinting

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    SIGSPATIAL '17 Paper Acceptance Rate 39 of 193 submissions, 20%;
    Overall Acceptance Rate 257 of 1,238 submissions, 21%

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    Cited By

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    • (2024)A Precise and Scalable Indoor Positioning System Using Cross-Modal Knowledge DistillationSensors10.3390/s2422732224:22(7322)Online publication date: 16-Nov-2024
    • (2024)A Quantum Access Points Selection Algorithm for Large-Scale Localization2024 IEEE 49th Conference on Local Computer Networks (LCN)10.1109/LCN60385.2024.10639748(1-7)Online publication date: 8-Oct-2024
    • (2023)Laser Range Scanners for Enabling Zero-overhead WiFi-based Indoor Localization SystemACM Transactions on Spatial Algorithms and Systems10.1145/35396599:1(1-25)Online publication date: 12-Jan-2023
    • (2023)SiMWiSense: Simultaneous Multi-Subject Activity Classification Through Wi-Fi Signals2023 IEEE 24th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM)10.1109/WoWMoM57956.2023.00019(46-55)Online publication date: Jun-2023
    • (2022)QLoc: A Realistic Quantum Fingerprint-based Algorithm for Large Scale Localization2022 IEEE International Conference on Quantum Computing and Engineering (QCE)10.1109/QCE53715.2022.00043(238-246)Online publication date: Sep-2022
    • (2022)Device-independent Quantum Fingerprinting for Large Scale Localization2022 20th Mediterranean Communication and Computer Networking Conference (MedComNet)10.1109/MedComNet55087.2022.9810400(208-215)Online publication date: 1-Jun-2022
    • (2022)A Quantum Algorithm for RF-based Fingerprinting Localization Systems2022 IEEE 47th Conference on Local Computer Networks (LCN)10.1109/LCN53696.2022.9843246(18-25)Online publication date: 26-Sep-2022
    • (2022)GRAFICS: Graph Embedding-based Floor Identification Using Crowdsourced RF Signals2022 IEEE 42nd International Conference on Distributed Computing Systems (ICDCS)10.1109/ICDCS54860.2022.00105(1051-1061)Online publication date: Jul-2022
    • (2021)SmartLOCProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34949725:4(1-24)Online publication date: 27-Dec-2021
    • (2021)Towards Quantum Computing for Location Tracking and Spatial SystemsProceedings of the 29th International Conference on Advances in Geographic Information Systems10.1145/3474717.3483958(278-281)Online publication date: 2-Nov-2021
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