Abstract
Mobile Crowd Sensing (MCS) allows an efficient collection of heterogeneous data over large areas, leveraging on the cooperation of MCS subscribers that offer services on their smartphones to this purpose. However, the coverage that a MCS platform can provide for a given area depends on the availability of subscribers and on their mobility in that area. To guarantee a better coverage, a MCS platform may employ a combination of static and mobile sensors and interpolation strategies that may provide meaningful data for all the area under observation. We discuss how two mechanisms (mixing static and mobile sensors and interpolation) can be combined together by using the large-scale mobility datasets of ParticipAct and the Weather Underground dataset.
| Original language | English |
|---|---|
| Title of host publication | 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud) |
| Publisher | IEEE |
| Pages | 102-108 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509063253 |
| DOIs | |
| Publication status | Published - 12 Jun 2017 |
| Event | 5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering 2017 - San Francisco, United States Duration: 7 Apr 2017 → 9 Apr 2017 |
Conference
| Conference | 5th IEEE International Conference on Mobile Cloud Computing, Services and Engineering 2017 |
|---|---|
| Abbreviated title | MobileCloud 2017 |
| Country/Territory | United States |
| City | San Francisco |
| Period | 7/04/17 → 9/04/17 |
Keywords
- Human Mobility
- Kriging
- Mobile Crowdsensing
ASJC Scopus subject areas
- Computer Networks and Communications
- Hardware and Architecture