  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.
