Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2517351.2517431 | SenSys |
Keywords | Field | DocType |
incomplete coverage,complete control,extrapolates missing piece,coverage gap,missing data,hurricane sandy,new york city gas,overall system design,available incomplete observation,measured phenomenon,energy harvesting,wireless networking,data aggregation | Data science,Wireless network,Computer security,Computer science,Metis,Systems design,Real-time computing,Extrapolation,Missing data,Participatory sensing,Data aggregator | Conference |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
14 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. Liu | 1 | 0 | 0.34 |
| Gu Shushi | 2 | 34 | 16.42 |
| C. Pan | 3 | 0 | 0.34 |
| W. Zheng | 4 | 0 | 0.34 |
| S. Li | 5 | 0 | 0.34 |
| Shaohan Hu | 6 | 499 | 30.70 |
| S Wang | 7 | 43 | 9.15 |
| Dong Wang | 8 | 865 | 62.74 |
| T AMIN | 9 | 2 | 1.06 |
| lu su | 10 | 1118 | 66.61 |
| Zongwu Xie | 11 | 40 | 14.59 |
| ramesh govindan | 12 | 15430 | 2144.86 |
| Amotz Bar-Noy | 13 | 285 | 67.94 |
| Tarek Abdelzaher | 14 | 10179 | 729.36 |