Being SMART about failures: assessing repairs in SMART homes
Being SMART about failures: assessing repairs in SMART homes
复制标题
对故障保持智能：评估智能家居的维修情况
DOI：
10.1145/2370216.2370225
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发表时间：
2012
期刊：
影响因子：
--
通讯作者：
S. Son
中科院分区：
文献类型：
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作者：
Krasimira Kapitanova;Enamul Hoque;J. Stankovic;K. Whitehouse;S. Son
廉价的无线传感产品大大降低了家庭传感的成本。然而，人们发现这些传感器经常发生故障，高昂的维护成本可能会抵消廉价硬件和自己动手安装的成本效益。在本文中，我们描述了一种名为SMART的新技术，该技术使用应用级语义来检测、评估和适应传感器故障。SMART通过分析多个分类器实例的相对行为，在运行时检测传感器故障，这些分类器实例经过训练，可以识别基于不同传感器子集的同一组活动。一旦检测到故障，SMART会评估其重要性并调整分类器集合，以避免维护调度。对来自两个公共数据集的三个家庭的评估表明，SMART平均减少了55%的维护调度次数，在运行时识别非故障停止故障的准确率超过85%，在传感器故障下的活动识别准确率平均提高了15%。
Inexpensive wireless sensing products are dramatically reducing the cost of in-home sensing. However, these sensors have been found to fail often and prohibitive maintenance costs may negate the cost benefits of inexpensive hardware and do-it-yourself installation. In this paper, we describe a new technique called SMART that uses application-level semantics to detect, assess, and adapt to sensor failures. SMART detects sensor failures at run-time by analyzing the relative behavior of multiple classifier instances trained to recognize the same set of activities based on different subsets of sensors. Once a failure is detected, SMART assesses its importance and adapts the classifier ensemble in attempt to avoid maintenance dispatch. Evaluation on three homes from two public datasets shows that SMART decreases the number of maintenance dispatches by 55% on average, identifies non-fail-stop failures at run-time with more than 85% accuracy, and improves the activity recognition accuracy under sensor failures by 15% on average.