On crowdsensed data acquisition using multi-dimensional point processes

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.


Published in:
Proceedings of the 31st IEEE International Conference on Data Engineering, Data Engineering Workshops (ICDEW), 124 - 128
Presented at:
31st IEEE International Conference on Data Engineering, Data Engineering Workshops (ICDEW), Seoul, South Korea, 13-17 April 2015
Year:
2015
Publisher:
IEEE
Keywords:
Laboratories:




 Record created 2015-09-19, last modified 2018-03-17

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