TY - GEN
T1 - GreenGPS
T2 - 8th Annual International Conference on Mobile Systems, Applications and Services, MobiSys 2010
AU - Ganti, Raghu K.
AU - Pham, Nam
AU - Ahmadi, Hossein
AU - Nangia, Saurabh
AU - Abdelzaher, Tarek F.
PY - 2010
Y1 - 2010
N2 - This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel-efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route.
AB - This paper develops a navigation service, called GreenGPS, that uses participatory sensing data to map fuel consumption on city streets, allowing drivers to find the most fuel-efficient routes for their vehicles between arbitrary end-points. The service exploits measurements of vehicular fuel consumption sensors, available via the OBD-II interface standardized in all vehicles sold in the US since 1996. The interface gives access to most gauges and engine instrumentation. The most fuel-efficient route does not always coincide with the shortest or fastest routes, and may be a function of vehicle type. Our experimental study shows that a participatory sensing system can influence routing decisions of individual users and also answers two questions related to the viability of the new service. First, can it survive conditions of sparse deployment? Second, how much fuel can it save? A challenge in participatory sensing is to generalize from sparse sampling of high-dimensional spaces to produce compact descriptions of complex phenomena. We illustrate this by developing models that can predict fuel consumption of a set of sixteen different cars on the streets of the city of Urbana-Champaign. We provide experimental results from data collection suggesting that a 1% average prediction error is attainable and that an average 10% savings in fuel can be achieved by choosing the right route.
KW - Green GPS
KW - Green navigation
KW - Model clustering
KW - Participatory sensing
UR - http://www.scopus.com/inward/record.url?scp=77954992140&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77954992140&partnerID=8YFLogxK
U2 - 10.1145/1814433.1814450
DO - 10.1145/1814433.1814450
M3 - Conference contribution
AN - SCOPUS:77954992140
SN - 9781605589855
T3 - MobiSys'10 - Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services
SP - 151
EP - 164
BT - MobiSys'10 - Proceedings of the 8th International Conference on Mobile Systems, Applications, and Services
Y2 - 15 June 2010 through 18 June 2010
ER -