Recent advances in AIoT technologies have led to an increasing popularity of utilizing machine learning algorithms to detect operational failures for cyber-physical systems (CPS). In its basic form, an anomaly detection module monitors the sensor measurements and actuator states from the physical plant, and detects anomalies in these measurements to identify abnormal operation status. Nevertheless, building effective anomaly detection models for CPS is rather challenging as the model has to accurately detect anomalies in presence of highly complicated system dynamics and unknown amount of sensor noise. In this work, we propose a novel time series anomaly detection method called Neural System Identification and Bayesian Filtering (NSIBF) in which a specially crafted neural network architecture is posed for system identification, i.e., capturing the dynamics of CPS in a dynamical state-space model; then a Bayesian filtering algorithm is naturally applied on top of the "identified" state-space model for robust anomaly detection by tracking the uncertainty of the hidden state of the system recursively over time. We provide qualitative as well as quantitative experiments with the proposed method on a synthetic and three real-world CPS datasets, showing that NSIBF compares favorably to the state-of-the-art methods with considerable improvements on anomaly detection in CPS.

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.

Service Integration plays an important role in Service-Oriented Architecture (SOA).With the thriving of SOA, many approaches have been proposed to address the issue. Several languages or specifications are introduced for the definition of integration plan, such as WSFL, BPML and BPEL4WS. And many recent researches employ semantic web techniques to enable the automatic service discovery and interoperation, OWL-S included. Most of the existing approaches are originally introduced for application developers or programmers to use, in which user requirement is usually expressed based on the services themselves. However, a service may be very complex, the functions of which are usually the composite and interconnection of several basic functions. It is sometimes difficult for normal users without developing skills to understand the services and then utilize them to define the integration solution. In the paper, we think that, the most familiar things for normal users are the functions they need and the working process they expect. Based on the intuitive idea, we propose a normal User-Centric Service Integration approach (UCSI). In the approach, a set of function elements are predefined for each specified service domain and three working process notations are introduced to model the relationships between function elements. Then user requirement and service function can both be represented as function elements interconnection based on the process notations. With the representation, the functionality relationship between user requirement and services can be resolved and the service integration solutions can be generated. Normal users with certain knowledge of specified service domains can try using the approach to achieve personalized service integration. And a prototype system is introduced in the end.