Towards Data Reliability Based on Triple Redundancy and Online Outlier Detection

Sylvain Poupry, Cédrick Béler, K. Medjaher
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引用次数: 1

Abstract

Today, air quality monitoring is a global concern. The World Health Organization (WHO) defined standards for each pollutant and each member state is committed to monitoring them continuously and reliably to protect the population. This responsibility is delegated to air quality monitoring associations. To achieve the objectives of reliable, accurate, and continuous measurements, these associations rely on conventional measuring stations with demanding specifications to serve as scientific references and decision supports for the authorities. However, because of heavy investments and required qualified staff, there are few stations and the coverage is coarse for territories of several thousand km2. To circumvent this difficulty, measurement network architectures using Low-Cost Sensors (LCS) have been deployed. Low cost and requiring less qualification, This alternative technology to conventional measuring stations makes it possible to target local pollution that could not otherwise be detected. Although it is more accurate on the spatial dimension, this technology has several drawbacks, notably in terms of measurement repeatability and hardware quality. It is also subject to measurement drifts over time. To overcome these drawbacks, a resilient and reliable architecture based on LCS and triple redundancy has been proposed. The basic principle is based on the implementation of three smart sensors (SmS) using LCS measuring the same parameters on the same perimeter. These SmS communicate with an Aggregator that aggregates the data sent by SmS. The aggregator includes also detection and voting tasks allowing to compare, cross the data, detect faults of LCS online, and provide data that are ready for processing. In this paper, a pre-processing algorithm in four steps is presented. It identifies hardware faults from one or more LCS and reports outliers for verification by an expert. It is configurable and can identify failure behaviors (LCS or air quality). Finally, the proposed algorithm excludes the outliers data from faulty LCS and presents only reliable ones.
基于三冗余和在线离群点检测的数据可靠性研究
今天,空气质量监测是一个全球关注的问题。世界卫生组织(世卫组织)为每一种污染物确定了标准,每个成员国都致力于持续可靠地监测这些标准,以保护人民。这一责任由空气质量监测协会承担。为了实现可靠、准确和连续测量的目标,这些协会依靠具有苛刻规格的传统测量站作为科学参考和决策支持。但是,由于大量投资和需要合格的工作人员,监测站很少,覆盖范围很广,只有几千平方公里。为了克服这一困难,已经部署了使用低成本传感器(LCS)的测量网络架构。成本低,资质要求低。这种替代传统测量站的技术使无法检测到的当地污染成为可能。虽然它在空间维度上更精确,但该技术有几个缺点,特别是在测量可重复性和硬件质量方面。随着时间的推移,它也会受到测量漂移的影响。为了克服这些缺点,提出了一种基于LCS和三重冗余的弹性可靠的体系结构。基本原理是基于使用LCS测量同一周界上相同参数的三个智能传感器(SmS)的实现。这些短信与聚合器通信,聚合器聚合短信发送的数据。聚合器还包括检测和投票任务,允许比较、交叉数据、在线检测LCS故障,并提供准备处理的数据。本文提出了一种分四步进行预处理的算法。它从一个或多个LCS中识别硬件故障,并报告异常值供专家验证。它是可配置的,可以识别故障行为(LCS或空气质量)。最后,该算法排除故障LCS中的异常数据,只给出可靠数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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