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.
Sensing Interpolation Strategies for a Mobile Crowdsensing Platform
Chessa, Stefano;
2017-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
