Participatory Sensing (PS) is a known paradigm of collaborative networks which provides incentives for users to participate in sensing tasks of Regions of Interest (RoIs). A challenge in wireless networking, however, is to balance the amount of data collected by users without imposing excessive load to the network. In this direction, this paper proposes a centralized system to adapt the sampling rate assigned to each crowdsourcing participant sensor. The sampling rate is computed based on the standard deviation of samples collected from a given RoI. The results obtained via simulations show a tradeoff between the sampling rate and the number of crowdsourcing participants. The more crowdsourcing participants, the lower must be the individual sampling rate and the amount of data transferred. This strategy can increase the data delivery rate taking into account the available short contact times, even though it requires a larger number of sensors.

参与式感知（PS）是一种协作网络的已知范例，它为用户提供了参与感兴趣区域（RoIs）感知任务的动机。然而，无线联网中的挑战是平衡用户收集的数据量，而不会对网络造成过多负载。为此，本文提出了一种集中式系统，以适应分配给每个众包参与者传感器的采样率。采样率是根据从给定RoI收集的样本的标准偏差计算得出的。通过仿真获得的结果表明，采样率与众包参与者的数量之间存在权衡。众包参与者越多，个人采样率和传输的数据量就必须越低。