Energy-efficient context classification with dynamic sensor control.
Energy-efficient context classification with dynamic sensor control.
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DOI：
10.1109/tbcas.2011.2166073
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发表时间：
2012-04
影响因子：
5.1
通讯作者：
Kaiser WJ
中科院分区：
文献类型：
--
作者：
Au LK;Bui AA;Batalin MA;Kaiser WJ
能源效率一直是可穿戴传感器系统的长期设计挑战。由于对紧凑尺寸和更好传感器的持续需求，它在连续受试者状态监测中尤其重要。提出了一种基于部分可观测马尔可夫决策过程（POMDP）的节能分类算法。在每一个时间步，POMDP动态选择传感器分类通过传感器选择策略。传感器选择问题被形式化为一个优化问题，其目标是在给定一定能量预算的情况下最小化误分类成本。状态转移建模为隐马尔可夫模型（HMM），和相应的传感器选择策略表示使用有限状态控制器（FSC）。为了评估这一框架，传感器数据收集多个主题在他们的自由生活条件。相对精度和能量减少所提出的方法进行比较，对朴素贝叶斯（始终在线）和简单的随机策略，以验证该算法的相对性能。当目标是保持相同的分类精度时，实现了显著的能量降低。
Energy efficiency has been a longstanding design challenge for wearable sensor systems. It is especially crucial in continuous subject state monitoring due to the ongoing need for compact sizes and better sensors. This paper presents an energy-efficient classification algorithm, based on partially observable Markov decision process (POMDP). In every time step, POMDP dynamically selects sensors for classification via a sensor selection policy. The sensor selection problem is formalized as an optimization problem, where the objective is to minimize misclassification cost given some energy budget. State transitions are modeled as a hidden Markov model (HMM), and the corresponding sensor selection policy is represented using a finite-state controller (FSC). To evaluate this framework, sensor data were collected from multiple subjects in their free-living conditions. Relative accuracies and energy reductions from the proposed method are compared against naïve Bayes (always-on) and simple random strategies to validate the relative performance of the algorithm. When the objective is to maintain the same classification accuracy, significant energy reduction is achieved.