Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-Based Continuous Event Processing
Optimizing Sensor Data Acquisition for Energy-Efficient Smartphone-Based Continuous Event Processing
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DOI：
10.1109/mdm.2011.76
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发表时间：
2011-06
期刊：
影响因子：
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通讯作者：
Archan Misra;Lipyeow Lim
中科院分区：
文献类型：
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作者：
Archan Misra;Lipyeow Lim
许多普遍的应用程序，例如活动识别或远程健康监测，利用个人移动设备（也称为智能手机）对从本地连接的可穿戴传感器获取的数据流进行连续处理。为了确保此类应用程序在电池有限的移动设备上连续运行，必须大幅减少与传感器数据采集和处理过程相关的能源开销。为了实现这一目标，本文引入了一种“获取成本”感知的连续查询处理技术，作为获取成本感知查询适应（ACQUA）框架的一部分。 ACQUA 使用基于拉的异步模型取代了当前的范例，其中数据通常从传感器流式传输（推送）到智能手机，其中，仅当流元素被判断为与正在处理的查询相关时，手机才从各个传感器检索适当的传感器数据块。我们描述了动态优化序列（对于具有合取和析取谓词的复杂流查询）的算法，其中基于单个传感器流的通信成本和选择性属性的组合，由手机检索此类传感器数据流。仿真实验表明，该方法可以在不影响处理逻辑保真度的情况下，使连续查询处理的能量开销降低70%。
Many pervasive applications, such as activity recognition or remote wellness monitoring, utilize a personal mobile device (aka smart phone) to perform continuous processing of data streams acquired from locally-connected, wearable, sensors. To ensure the continuous operation of such applications on a battery-limited mobile device, it is essential to dramatically reduce the energy overhead associated with the process of sensor data acquisition and processing. To achieve this goal, this paper introduces a technique of ‘acquisition-cost' aware continuous query processing, as part of the Acquisition Cost-Aware Query Adaptation (ACQUA) framework. ACQUA replaces the current paradigm, where the data is typically streamed (pushed) from the sensors to the smart phone, with a pull-based asynchronous model, where the phone retrieves appropriate blocks of sensor data from individual sensors, only when the stream elements are judged to be relevant to the query being processed. We describe algorithms that dynamically optimize the sequence (for complex stream queries with conjunctive and disjunctive predicates) in which such sensor data streams are retrieved by the phone, based on a combination of the communication cost and selectivity properties of individual sensor streams. Simulation experiments indicate that this approach can result in 70% reduction in the energy overhead of continuous query processing, without affecting the fidelity of the processing logic.