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Towards participatory sensing of regions of interest with adaptive sampling rate

机译：对适应性采样率的对兴趣区域的参与感性

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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. (C) 2020 Elsevier Inc. All rights reserved.
机译：参与式传感（PS）是一个已知的协作网络范式，其为用户提供了参与感兴趣区域的传感任务（ROI）的激励措施。然而，无线网络中的挑战是平衡用户收集的数据量而不会对网络强加过多负载。在此方向上，本文提出了一种集中系统，以使分配给每个众包参与者传感器的采样率。基于从给定ROI收集的样本的标准偏差来计算采样率。通过模拟获得的结果显示了采样率与众包参与者的数量之间的权衡。众所周心的参与者，较低的必须是单独的采样率和转移的数据量。此策略可以提高数据传递率，以考虑到可用的短接触时间，即使它需要更多的传感器。 （c）2020 Elsevier Inc.保留所有权利。

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