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One of the main benefits of mobile participatory sensing becoming a reality is the increased knowledge it will provide about the real world while relying on a large number of mobile devices. Those devices can host different types of sensors incorporated in every aspect of our lives. However, given the increasing number of capable mobile devices, any participatory sensing approach should be, first and foremost, scalable. To address this challenge, we present an approach to decrease the participation of (sensing) devices in a manner that does not compromise the accuracy of the real-world information while increasing the efficiency of the overall system. To reduce the number of the devices involved, we present a probabilistic registration approach, based on a realistic human mobility model, that allows devices to decide whether or not to register their sensing services depending on the probability of other, equivalent devices being present at the locations of their expected path. We present the design and implementation of a registration middleware based on our techniques, using which mobile devices can base their registration decision. Through experiments performed on real and simulated datasets, we show that our approach scales, while not sacrificing significant amounts of sensing coverage.