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

TLDR

PEIR is a participatory sensing app that uses mobile phone GPS data to estimate personalized environmental impact and exposure, exemplifying mobile systems that combine web processing with personal reach. This paper documents and evaluates the running PEIR system and outlines new algorithmic components for integration, such as map‑matching, GSM‑augmented activity classification, and a selective hiding mechanism. PEIR collects GPS traces, classifies activity with an HMM, segments trips, incorporates traffic, weather, and context data, computes impact using established models, and provides a user interface with usage statistics. A two‑month snapshot of system use yielded usage statistics demonstrating the platform’s practical deployment.

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.