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Publication
ICDEW 2015
Conference paper
On crowdsensed data acquisition using multi-dimensional point processes
Abstract
Crowdsensing 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.