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Markov-optimal Sensing Policy for User State Estimation in Mobile Devices

机译：Markov-移动设备中用户状态估计的最佳感应策略

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摘要

Mobile device based human-centric sensing and user state recognition provide rich contextual information for various mobile applications and services. However, continuously capturing this contextual information consumes significant amount of energy and drains mobile device battery quickly. In this paper, we propose a computationally efficient algorithm to obtain the optimal sensor sampling policy under the assumption that the user state transition is Markovian. This Markov-optimal policy minimizes user state estimation error while satisfying a given energy consumption budget. We first compare the Markov-optimal policy with uniform periodic sensing for Markovian user state transitions and show that the improvements obtained depend upon the underlying state transition probabilities. We then apply the algorithm to two different sets of real experimental traces pertaining to user motion change and inter-user contacts and show that the Markov-optimal policy leads to an approximately 20% improvement over the naive uniform sensing policy.
机译：基于移动设备的人以人为本的感应和用户状态识别提供了各种移动应用程序和服务的丰富上下文信息。然而，连续捕获这种上下文信息消耗大量的能量，并快速排出移动设备电池。在本文中，我们提出了一种计算上有效的算法，以便在用户状态转换是马尔可维亚的假设下获得最佳传感器采样策略。此马尔可夫 - 最佳策略最小化用户状态估计误差，同时满足给定的能耗预算。我们首先比较Markov-Optimal策略对Markovian用户状态转换的统一定期感测，并表明所获得的改进取决于底层状态转换概率。然后，我们将该算法应用于两个不同的实验迹线，与用户运动变化和用户间联系人相关，并显示马尔可夫 - 最佳政策导致天真均匀的传感政策的提高大约20％。

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