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

机译：移动设备中用户状态估计的马尔可夫最优传感策略

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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：基于移动设备的以人为本的感知和用户状态识别为各种移动应用程序和服务提供了丰富的上下文信息。但是，连续捕获此上下文信息会消耗大量能量，并且会很快耗尽移动设备的电池电量。在本文中，我们提出了一种计算有效的算法，以在用户状态转换为马尔可夫假设的情况下获得最佳传感器采样策略。这种马尔可夫最优策略在满足给定能耗预算的同时，将用户状态估计误差降至最低。我们首先将具有统一周期感测的Markov最优策略与Markovian用户状态转换进行比较，并表明获得的改进取决于基础状态转换概率。然后，我们将该算法应用于与用户运动变化和用户间接触有关的两组不同的实际实验轨迹，并表明，马尔可夫最优策略比单纯的统一感知策略带来了大约20％的改进。

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来源
《9th ACM/IEEE international conference on information processing in sensor networks 2010》|2010年|p.268-278|共11页
会议地点 Stockholm(SE);Stockholm(SE)
作者
Yi Wang; Bhaskar Krishnamacharit; Qing Zhao; Murali Annavaram;
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作者单位

Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA;

Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA;

Department of Electrical and Computer Engineering, University of California, Davis, USA;

Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, USA;

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原文格式 PDF
正文语种 eng
中图分类信息处理（信息加工）;
关键词
energy efficiency; mobile sensing; optimal sampling policy; markovian user state;

机译：能源效率;移动感应；最佳抽样政策；马尔可夫用户状态;

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4. Markov-optimal Sensing Policy for User State Estimation in Mobile Devices [C] . Yi Wang, Bhaskar Krishnamacharit, Qing Zhao, ACM/IEEE international conference on information processing in sensor networks . 2010

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5. Considering mobile devices, context awareness, and mobile users . [D] . Su, Jing. 2010

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7. MemorySense: Reconstructing and ranking user memories on mobile devices [O] . Karl Aberer, Michele Catasta, Horia Radu, 2014

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

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