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SurroundSense: mobile phone localization via ambience fingerprinting

Published: 20 September 2009 Publication History
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  • Abstract

    A growing number of mobile computing applications are centered around the user's location. The notion of location is broad, ranging from physical coordinates (latitude/longitude) to logical labels (like Starbucks, McDonalds). While extensive research has been performed in physical localization, there have been few attempts in recognizing logical locations. This paper argues that the increasing number of sensors on mobile phones presents new opportunities for logical localization. We postulate that ambient sound, light, and color in a place convey a photo-acoustic signature that can be sensed by the phone's camera and microphone. In-built accelerometers in some phones may also be useful in inferring broad classes of user-motion, often dictated by the nature of the place. By combining these optical, acoustic, and motion attributes, it may be feasible to construct an identifiable fingerprint for logical localization. Hence, users in adjacent stores can be separated logically, even when their physical positions are extremely close. We propose SurroundSense, a mobile phone based system that explores logical localization via ambience fingerprinting. Evaluation results from 51 different stores show that SurroundSense can achieve an average accuracy of 87% when all sensing modalities are employed. We believe this is an encouraging result, opening new possibilities in indoor localization.

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    cover image ACM Conferences
    MobiCom '09: Proceedings of the 15th annual international conference on Mobile computing and networking
    September 2009
    368 pages
    ISBN:9781605587028
    DOI:10.1145/1614320
    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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    Publication History

    Published: 20 September 2009

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    Author Tags

    1. context
    2. fingerprinting
    3. localization
    4. mobile phones

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    • (2024)Robust Indoor Location Identification for Smartphones Using Echoes From Dominant ReflectorsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3307695(1-17)Online publication date: 2024
    • (2024)WiCloak: Protect Location Privacy of WiFi Devices2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00013(101-112)Online publication date: 13-May-2024
    • (2024)GlitchOS: An Open Source Virtual Assistant2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC60891.2024.10427588(0133-0137)Online publication date: 8-Jan-2024
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