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

  • Authors:

  • Min Mun;Sasank Reddy;Katie Shilton;Nathan Yau;Jeff Burke;Deborah Estrin;Mark Hansen;Eric Howard;Ruth West;Péter Boda

  • Affiliations:

  • University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;University of California, Los Angeles, Los Angeles, USA;Nokia Research Center Palo Alto, Palo Alto, USA

  • Venue:
  • Proceedings of the 7th international conference on Mobile systems, applications, and services
  • Year:
  • 2009

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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.