000211539 001__ 211539
000211539 005__ 20180317094516.0
000211539 0247_ $$2doi$$a10.1109/ICDEW.2015.7129562
000211539 037__ $$aCONF
000211539 245__ $$aOn crowdsensed data acquisition using multi-dimensional point processes
000211539 269__ $$a2015
000211539 260__ $$bIEEE$$c2015
000211539 336__ $$aConference Papers
000211539 520__ $$aCrowdsensing applications are increasing at a tremendous rate. In crowdsensing, mobile sensors (humans, vehicle-mounted sensors, etc.) generate streams of information that is used for inferring high-level phenomena of interest (e.g, traffic jams, air pollution). Unlike traditional sensor network data, crowdsensed data has a highly skewed spatio-temporal distribution caused largely due to the mobility of sensors [1]. Thus, designing systems that can mitigate this effect by acquiring crowdsensed at a fixed spatio-temporal rate are needed. In this paper we propose using multi-dimensional point processes (MDPPs), a mathematical modeling tool that can be effectively used for performing this data acquisition task.
000211539 6531_ $$aCrowd-Sensing
000211539 6531_ $$aMobile Sensing
000211539 6531_ $$aData Acquisition
000211539 700__ $$0242022$$aSathe, Saket$$g177954
000211539 700__ $$aSellis, Timos
000211539 700__ $$0240941$$aAberer, Karl$$g134136
000211539 7112_ $$a31st IEEE International Conference on Data Engineering, Data Engineering Workshops (ICDEW)$$cSeoul, South Korea$$d13-17 April 2015
000211539 773__ $$q124 - 128$$tProceedings of the 31st IEEE International Conference on Data Engineering, Data Engineering Workshops (ICDEW)
000211539 8564_ $$s517094$$uhttps://infoscience.epfl.ch/record/211539/files/07129562.pdf$$yn/a$$zn/a
000211539 909CO $$ooai:infoscience.tind.io:211539$$pIC$$pconf
000211539 909C0 $$0252004$$pLSIR$$xU10405
000211539 917Z8 $$x134136
000211539 937__ $$aEPFL-CONF-211539
000211539 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000211539 980__ $$aCONF