SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring
SmartDC: Mobility Prediction-Based Adaptive Duty Cycling for Everyday Location Monitoring
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DOI：
10.1109/tmc.2013.14
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发表时间：
2014-03-01
影响因子：
7.9
通讯作者：
Cha, Hojung
中科院分区：
文献类型：
--
作者：
Chon, Yohan;Talipov, Elmurod;Cha, Hojung
在日常生活中监视用户的移动性是提供高级移动服务的必不可少的要求。尽管已经进行了广泛的尝试来监视用户的移动性，但以前的工作很少通过对实际部署的时间行为的预测来解决问题。在本文中，我们介绍了SmartDC，这是一种基于移动性预测的自适应骑行计划，以提供有关用户移动性的上下文信息：时间分辨的位置和路径。与以前的方法不同的方法是最大程度地减少跟踪原始坐标的能量消耗，我们提出了有效的技术，以最大程度地利用给定能量限制来监测有意义的地方的准确性。 SMARTDC包括无监督的移动性学习者，移动性预测指标和基于马尔可夫决策过程的自适应税单行。 SMARTDC估计单个移动性的规律性，并预测位置的停留时间，以确定有效的感应时间表。我们的实验结果表明，SMARTDC的消耗比定期感应方案要少81％，而使用上下文感知感应的方案比定期感应方案要少87％，但它仍然正确地监视了用户位置的90％在160秒的延迟中变化。
Monitoring a user's mobility during daily life is an essential requirement in providing advanced mobile services. While extensive attempts have been made to monitor user mobility, previous work has rarely addressed issues with predictions of temporal behavior in real deployment. In this paper, we introduce SmartDC, a mobility prediction-based adaptive duty cycling scheme to provide contextual information about a user's mobility: time-resolved places and paths. Unlike previous approaches that focused on minimizing energy consumption for tracking raw coordinates, we propose efficient techniques to maximize the accuracy of monitoring meaningful places with a given energy constraint. SmartDC comprises unsupervised mobility learner, mobility predictor, and Markov decision process-based adaptive duty cycling. SmartDC estimates the regularity of individual mobility and predicts residence time at places to determine efficient sensing schedules. Our experiment results show that SmartDC consumes 81 percent less energy than the periodic sensing schemes, and 87 percent less energy than a scheme employing context-aware sensing, yet it still correctly monitors 90 percent of a user's location changes within a 160-second delay.