PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

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PEIR, the personal environmental impact report, as a platform for participatory sensing systems research

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International Conference On Mobile Systems, Applications And Services archive
Proceedings of the 7th international conference on Mobile systems, applications, and services table of contents

Kraków, Poland

SESSION: Experimental platforms table of contents

Pages 55-68

Year of Publication: 2009

ISBN:978-1-60558-566-6

Authors

Min Mun	University of California, Los Angeles, Los Angeles, USA
Sasank Reddy	University of California, Los Angeles, Los Angeles, USA
Katie Shilton	University of California, Los Angeles, Los Angeles, USA
Nathan Yau	University of California, Los Angeles, Los Angeles, USA
Jeff Burke	University of California, Los Angeles, Los Angeles, USA
Deborah Estrin	University of California, Los Angeles, Los Angeles, USA
Mark Hansen	University of California, Los Angeles, Los Angeles, USA
Eric Howard	University of California, Los Angeles, Los Angeles, USA
Ruth West	University of California, Los Angeles, Los Angeles, USA
Péter Boda	Nokia Research Center Palo Alto, Palo Alto, USA

Sponsors

SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery

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ACM New York, NY, USA

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ABSTRACT

PEIR, the Personal Environmental Impact Report, is a participatory sensing application that uses location data sampled from everyday mobile phones to calculate personalized estimates of environmental impact and exposure. It is an example of an important class of emerging mobile systems that combine the distributed processing capacity of the web with the personal reach of mobile technology. This paper documents and evaluates the running PEIR system, which includes mobile handset based GPS location data collection, and server-side processing stages such as HMM-based activity classification (to determine transportation mode); automatic location data segmentation into "trips''; lookup of traffic, weather, and other context data needed by the models; and environmental impact and exposure calculation using efficient implementations of established models. Additionally, we describe the user interface components of PEIR and present usage statistics from a two month snapshot of system use. The paper also outlines new algorithmic components developed based on experience with the system and undergoing testing for integration into PEIR, including: new map-matching and GSM-augmented activity classification techniques, and a selective hiding mechanism that generates believable proxy traces for times a user does not want their real location revealed.

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