enviroCar: A Citizen Science Platform for Analyzing and Mapping Crowd-Sourced Car Sensor Data


  • Acknowledgements: This work has been financially supported by the project Flexible and Efficient Integration of Sensors and Sensor Web Services funded by the ERDF program for NRW (contract number N 114/2008), as well as the 52 math formulaNorth Initiative for Geospatial Open Source Software. Many thanks go to the enviroCar project team (http://www.envirocar.org) and their enthusiastic work on implementing the approach presented here. Finally, we thank all the sponsors of the enviroCar crowd-funding initiative (http://www.indiegogo.com/projects/envirocar) for supporting us in building this new source of environmental data.


This article presents the enviroCar platform for collecting geographic data acquired from automobile sensors and openly providing those data for further processing and analysis. By plugging a low-cost On-Board Diagnostics (OBD-II) adapter into a car and using an Android smartphone, various kinds of sensor data measured by today's cars can be collected and uploaded on to the Web. Once available on the Web, these data can be used to monitor traffic and related environmental parameters. We analyse the OBD-II interface and its potential usage for environmental monitoring, e.g. to estimate fuel consumption and resulting math formula emissions, noise emission, and standing times. Next, we present the main contribution of this article, the system design of the enviroCar platform. This system design consists of the enviroCar app and the enviroCar server, which allows for flexible geoprocessing of the uploaded data. We focus in this article on the description of the spatiotemporal RESTful Web Service interface and underlying data model specifically designed for handling the mobile sensor data. Finally, we present application scenarios in which the enviroCar platform can act as a powerful tool, e.g. regarding traffic monitoring and smarter cities (e.g. the detection of pollutant emission hotspots in the city), or towards applications for a quantified self (e.g. monitoring fuel consumption). We started the enviroCar project in 2013 and have been able to attract a growing number of participants since then. In a crowd-funding initiative, enviroCar was successfully funded by volunteers, demonstrating the interest in this platform.