The mobile crowdsourcing technology has been widely researched and applied with the wide popularity of smartphones in recent years. In the applications, the smartphone and its user act as a whole, which called as the composite node in this article. Since smartphone is usually under the operation of its user, the user’s participation cannot be excluded out the applications. But there are a few works noticed that humans and their smartphones depend on each other. In this article, we first present the relation between the smartphone and its user as the conditional decision and sensing. Under this relation, the composite node performs the sensing decision of the smartphone which based on its user’s decision. Then, this article studies the performance of the composite sensing process under the scenario which composes of an application server, some objects, and users. In the progress of the composite sensing, users report their sensing results to the server. Then, the server returns rewards to some users to maximize the overall reward. Under this scenario, this article maps the composite sensing process as the partially observable Markov decision process, and designs a composite sensing solution for the process to maximize the overall reward. The solution includes optimal and myopic policies. Besides, we provide necessary theoretical analysis, which ensures the optimality of the optimal algorithm. In the end, we conduct some experiments to evaluate the performance of our two policies in terms of the average quality, the sensing ratio, the success report ratio, and the approximate ratio. In addition, the delay and the progress proportion of optimal policy are analyzed. In all, the experiments show that both policies we provide are obviously superior to the random policy.

中文翻译：

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众包复合传感的最优策略

近年来，随着智能手机的广泛普及，移动众包技术得到了广泛的研究和应用。在应用程序中，智能手机及其用户作为一个整体，在本文中称为复合节点。由于智能手机通常在其用户的操作下，因此不能将用户的参与排除在应用程序之外。但是有一些作品注意到人类和他们的智能手机相互依赖。在本文中，我们首先将智能手机与其用户之间的关系呈现为条件决策和感知。在这种关系下，复合节点根据其用户的决定执行智能手机的感知决定。然后，本文研究了由应用服务器组成的场景下复合感知过程的性能，一些对象和用户。在复合感知的过程中，用户向服务器报告他们的感知结果。然后，服务器将奖励返回给一些用户，以最大化整体奖励。在这种场景下，本文将复合感知过程映射为部分可观察的马尔可夫决策过程，并为该过程设计复合感知解决方案以最大化整体奖励。该解决方案包括最优策略和短视策略。此外，我们提供了必要的理论分析，保证了最优算法的最优性。最后，我们进行了一些实验，从平均质量、感知比率、成功报告比率和近似比率方面评估我们两个策略的性能。此外，还分析了最优策略的延迟和进度比例。在所有，