ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems
ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems
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DOI：
10.1109/jiot.2017.2766085
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发表时间：
2018-04-01
影响因子：
10.6
通讯作者：
Mahajan, Sachit
中科院分区：
文献类型：
--
作者：
Chen, Ling-Jyh;Ho, Yao-Hua;Mahajan, Sachit
随着城市人口密度的持续增长，空气质量正在恶化，并成为一个严重的问题。空气污染，特别是细颗粒物(PM2.5)，已经引起了一系列对公众健康的关注。因此，一些大规模、低成本的PM2.5监测系统已经部署在几个国际智慧城市项目中。这类环境传感系统的主要挑战之一是确保数据质量。本文提出了一种用于大规模真实环境感知系统的异常检测框架(ADF)。该框架由四个模块组成：1)时间切片异常检测(TSAD)，用于检测实时传感器测量数据流中的空间、时间和时空异常；2)实时发射检测，用于检测潜在的区域发射源；3)设备排名，用于为每个传感设备提供排名；以及4)故障检测，用于识别故障设备。使用Airbox项目的真实测量数据，我们证明了该框架可以有效地识别原始测量数据中的离群值，并推断出公众和政府当局可以感知的异常事件。由于其设计简单，ADF具有高度的可扩展性，可用于其他高级应用，并可用于支持各种大规模环境传感系统。
As the population density continues to grow in the urban settings, air quality is degrading and becoming a serious issue. Air pollution, especially fine particulate matter (PM2.5), has raised a series of concerns for public health. As a result, a number of large-scale, low cost PM2.5 monitoring systems have been deployed in several international smart city projects. One of the major challenges for such environmental sensing systems is ensuring the data quality. In this paper, we propose an anomaly detection framework (ADF) for large-scale, real-world environmental sensing systems. The framework is composed of four modules: 1) time-sliced anomaly detection (TSAD), which detects spatial, temporal, and spatio-temporal anomalies in the real-time sensor measurement data stream; 2) real-time emission detection, which detects potential regional emission sources; 3) device ranking, which provides a ranking for each sensing device; and 4) malfunction detection, which identifies malfunctioning devices. Using real world measurement data from the AirBox project, we demonstrate that the proposed framework can effectively identify outliers in the raw measurement data as well as infer anomalous events that are perceivable by the general public and government authorities. Because of its simple design, ADF is highly extensible to other advanced applications, and it can be exploited to support various large-scale environmental sensing systems.