H. Liu
R. Govindan
ABSTRACT
In this demo, a learning system, called Metis, is presented that extrapolates missing pieces in participatory sensing data. The work addresses the challenge of incomplete coverage in participatory sensing applications, where lack of complete control over participant mobility and sensing patterns may create coverage gaps in space and in time. Metis learns the underlying spatiotemporal patterns of the measured phenomenon from available incomplete observations, and uses these patterns to infer missing data. We describe the overall system design and demonstrate the system using data collected during the New York City gas crisis in the aftermath of Hurricane Sandy.
- J. Burke, D. Estrin, M. Hansen, A. Parker, N. Ramanathan, S. Reddy, and M. B. Srivastava. Participatory sensing. In World Sensor Web Workshop, ACM Sensys 2006, Boulder, Colorado, October 31, 2006.Google Scholar
- All Hazards Consortium. URL: www.ahcusa.org/.Google Scholar
- GasBuddy: Find Low Gas Prices in the USA and Canada. URL: www.gasbuddy.com/.Google Scholar
Recommendations
Data Extrapolation in Social Sensing for Disaster Response
DCOSS '14: Proceedings of the 2014 IEEE International Conference on Distributed Computing in Sensor SystemsThis paper complements the large body of social sensing literature by developing means for augmenting sensing data with inference results that "fill-in" missing pieces. Unlike trend-extrapolation methods, we focus on prediction in disaster scenarios ...
Big data management in participatory sensing: Issues, trends and future directions
AbstractParticipatory sensing has become an emerging technology of this era owing to its low cost in big sensor data collection. Prior to participatory sensing, large-scale deployment complexities were found in wireless sensor networks when ...
Highlights- This paper highlights big data management issues in participatory sensing.
- We ...
Maximizing Data Credibility Under Budget Constraint for Participatory Sensing
HPCC-CSS-ICESS '15: Proceedings of the 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conf on Embedded Software and SystemsParticipatory sensing campaigns encourage ordinary people to collect and share sensing data by using their portable smart devices like smartphones, iPad and iWatch. However, compared with static sensors that are deployed in the particular places by ...
Comments