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Proximity classification for mobile devices using wi-fi environment similarity

Published: 19 September 2008 Publication History

Abstract

This paper describes an algorithm to compute lists of people and devices that are physically nearby to a mobile user based on the analysis of signals from existing wireless networks. The system evaluates proximity by classifying the degree of similarity of the Wi-Fi scan data through a statistical Gaussian Mixture Model. It recognizes when the devices are in the same area, and, in this case, it distinguishes three proximity levels: High (e.g. same room), Medium (e.g. same floor) and Low (e.g. same building). The algorithm can be deployed on a remote server that receives Wi-Fi scanning data (including MAC addresses and signal strength) from mobile devices. The server estimates proximity by extracting a set of features from each received pair of Wi-Fi data, feeding them to the GMM model and selecting the category with greatest probability. The method presented in the paper does not require calibration and leverages on existing Wi-Fi signals, while obtaining a percentage of correct discrimination among three levels near to 90%.

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  • (2022)User-Centric Proximity Estimation Using Smartphone Radio FingerprintingSensors10.3390/s2215560922:15(5609)Online publication date: 27-Jul-2022
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  • (2022)WiFi and BLE Fingerprinting for Smartphone Proximity Detection2022 6th European Conference on Electrical Engineering & Computer Science (ELECS)10.1109/ELECS55825.2022.00029(130-139)Online publication date: Dec-2022
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    cover image ACM Conferences
    MELT '08: Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
    September 2008
    142 pages
    ISBN:9781605581897
    DOI:10.1145/1410012
    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: 19 September 2008

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    Author Tags

    1. location-based services
    2. proximiry measurement
    3. wi-fi

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    • (2022)User-Centric Proximity Estimation Using Smartphone Radio FingerprintingSensors10.3390/s2215560922:15(5609)Online publication date: 27-Jul-2022
    • (2022)Comparing Contact Tracing Through Bluetooth and GPS Surveillance Data: A Simulation-driven Approach (Preprint)Journal of Medical Internet Research10.2196/38170Online publication date: 1-Apr-2022
    • (2022)WiFi and BLE Fingerprinting for Smartphone Proximity Detection2022 6th European Conference on Electrical Engineering & Computer Science (ELECS)10.1109/ELECS55825.2022.00029(130-139)Online publication date: Dec-2022
    • (2022)Smartphone Proximity Detection Using WiFi and BLE Fingerprinting2022 International Balkan Conference on Communications and Networking (BalkanCom)10.1109/BalkanCom55633.2022.9900709(36-40)Online publication date: 22-Aug-2022
    • (2022)Machine Learning and Deep Learning for Hardware FingerprintingSecurity and Artificial Intelligence10.1007/978-3-030-98795-4_9(181-213)Online publication date: 8-Apr-2022
    • (2020)Epidemic contact tracing with smartphone sensorsJournal of Location Based Services10.1080/17489725.2020.1805521(1-37)Online publication date: 1-Sep-2020
    • (2019)Outdoor Places of Interest Recognition Using WiFi FingerprintsIEEE Transactions on Vehicular Technology10.1109/TVT.2019.290536368:5(5076-5086)Online publication date: May-2019
    • (2019)Let's Meet: A Smartphone Co-Navigation System Based on Relative Direction and Proximity Change for Indoor Environments2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City; IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)10.1109/HPCC/SmartCity/DSS.2019.00355(2535-2542)Online publication date: Aug-2019
    • (2018)Estimating the Physical Distance between Two Locations with Wi-Fi Received Signal Strength Information Using Obstacle-aware ApproachProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649402:3(1-26)Online publication date: 18-Sep-2018
    • (2018)ROOMMATEs: An Unsupervised Indoor Peer Discovery Approach for LTE D2D CommunicationsIEEE Transactions on Vehicular Technology10.1109/TVT.2018.283222367:6(5069-5083)Online publication date: Jun-2018
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