GreenGPS: a participatory sensing fuel-efficient maps application

By Raghu Kiran Ganti, Nam Minh Pham, Hossein Ahmadi, Saurabh Nangia, Tarek F. Abdelzaher. ACM SIGMOBILE International Conference on Mobile Systems, Applications, and Services, 2010

381 citations recorded by the source corpus.

Subfields: Building and Construction, Transportation, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e3829353f668d9ba09b444447ff74212c739412c

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Citations in the corpus

Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems (2018, background): “In intelligent transportation systems[1], some example applications are green navigation service[2], transit tracking[3] and data map construction[4].”
Optimal incentive-driven design of participatory sensing systems (2013, methodology): “In the second category, an example is GreenGPS [4], which uses a vehicle interface to measure and transmit fuel consumption and location data.”
Deanonymizing mobility traces: using social network as a side-channel (2012, methodology): “, location, acceleration) generated by everyday mobile devices such as smartphones, tablets, and cars have become quite popular [3, 11, 15, 21, 32].”
On truth discovery in social sensing: A maximum likelihood estimation approach (2012, background): “More recent work has focused on addressing the challenges of preserving privacy and building general models in sparse and multi-dimensional social sensing space [2], [3], [14].”
A survey on privacy in mobile participatory sensing applications (2011, background): “For example, the captured data can be aggregated among several participants to render them indistinguishable or published in the form of statistics [38] and maps [12].”
SignalGuru: leveraging mobile phones for collaborative traffic signal schedule advisory (2011, background): “Several systems have been proposed that leverage GPS, accelerometer and proximity sensors in order to estimate traffic conditions [15, 23, 27], detect road abnormalities [11], collect information for available parking spots [22] and compute fuel efficient routes [12].”
Inferring human mobility patterns from taxicab location traces (2013, positive): “Applications that are driven by the analysis of geo-tagged sensor data generated by everyday mobile devices such as smartphones, tablets, and cars have become quite popular in the recent past [5, 10, 13].”
Mobile computing: the next decade (2010, background): “The importance of crowd sourcing as a fundamental technique in mobile computing is already well-recognized, as shown by a sampling of recent papers in mobile computing conferences and workshops [4, 11, 18, 34, 35, 36].”
When things matter: A survey on data-centric internet of things (2016, background): “GreenGPS, a navigation service that uses participatory sensing data to map fuel consumption on city streets, has been designed by Ganti et al. (2010).”