RelSen：一种基于优化的传感器可靠性监测和数据清洗框架
RelSen: An Optimization-based Framework for Simultaneously Sensor Reliability Monitoring and Data Cleaning
摘要：
物联网（IoT）技术的最新进展导致传感应用的普及率激增。因此，人们在日常生活中越来越依赖从传感器获得的信息来做出决策。不幸的是，在大多数传感应用中，已知传感器是容易出错的，并且它们的测量可能在任何意想不到的时间变得误导。因此，为了提高传感应用的可靠性，除了感兴趣的物理现象/过程之外，我们认为在对传感器进行分析之前监控传感器的可靠性并清理传感器数据也非常重要。现有的研究往往把传感器的可靠性监测和传感器数据清洗作为单独的问题。在这项工作中，我们提出了RelSen，一种新的基于优化的框架，同时解决这两个问题，通过利用它们之间的相互依赖。此外，RelSen不是特定于应用程序的，因为其实现假设了对监控下的过程动态的最小先验知识。这大大提高了它在实践中的通用性和适用性。在我们的实验中，我们将RelSen应用于室外空气污染监测系统和水泥回转窑的状态监测系统。实验结果表明，我们的框架可以及时识别不可靠的传感器，并消除传感器测量误差所造成的三种类型的最常见的传感器故障。
英文摘要：
Recent advances in the Internet of Things (IoT) technology have led to a surge on the popularity of sensing applications. As a result, people increasingly rely on information obtained from sensors to make decisions in their daily life. Unfortunately, in most sensing applications, sensors are known to be error-prone and their measurements can become misleading at any unexpected time. Therefore, in order to enhance the reliability of sensing applications, apart from the physical phenomena/processes of interest, we believe it is also highly important to monitor the reliability of sensors and clean the sensor data before analysis on them being conducted. Existing studies often regard sensor reliability monitoring and sensor data cleaning as separate problems. In this work, we propose RelSen, a novel optimization-based framework to address the two problems simultaneously via utilizing the mutual dependence between them. Furthermore, RelSen is not application-specific as its implementation assumes a minimal prior knowledge of the process dynamics under monitoring. This significantly improves its generality and applicability in practice. In our experiments, we apply RelSen on an outdoor air pollution monitoring system and a condition monitoring system for a cement rotary kiln. Experimental results show that our framework can timely identify unreliable sensors and remove sensor measurement errors caused by three types of most commonly observed sensor faults.
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