UC Irvine

The cyber-physical system (CPS) is a term describing a broad range of complex, multi-disciplinary, physically-aware next generation engineered systems that integrate embedded computing technologies into the physical world. Sensors play an important role in this integration because they provide the data extracted from the physical world for the cyber systems. However, this process is likely to be misled by incorrect data due to sensor faults.

In this dissertation, the main focus is on sensor fault mitigation and achieving high reliability in CPS operations. One of the challenges we tackle is timely event detection in CPS under faulty sensor conditions. In this regard, we examine the falling ball example (FBE) using binary event detectors, a controller, and a camera for timely motion detection and estimation of a falling ball. Another challenge we tackle is satisfying thermal comfort and energy efficiency under faulty sensor conditions in a multi-room building incorporating temperature sensors, controllers, and heating, ventilation, and air conditioning (HVAC) systems. For both cases, we adopt a model-based design (MBD) methodology to analyze the effect of sensor faults on the system outcome. In this regard, we develop well-defined fault and system evaluation models and incorporate them into the traditional CPS model that comprises the cyber, interface (e.g., sensors and actuators) and physical models.

We explore various fault mitigation techniques based on redundancies and temporal-spatial correlations between sensors' data in a holistic design perspective. Furthermore, considering compute demands of CPSs, we introduce the XGRID embedded many-core architecture. XGRID makes use of a novel, FPGA-like, programmable interconnect infrastructure offering scalability and deterministic communication. We further introduce a deployment scenario of XGRID as a use case for thermal control of the multi-room building.

Our findings regarding reliable CPS design show that the physical system attributes can be more dominant than the cyber system attributes on the system outcome. In addition, sensor faults may lead to unsatisfactory system outcome since CPSs heavily rely on sensor readings for decision making. Therefore, the analysis of temporal and spatial correlations between sensor readings helps mitigate sensor faults and enable CPSs to utilize sensors' data more efficiently for decision making.