부산대학교 도서관

Industrial Internet of Things (IIoT)-based control is growing rapidly, such as smart factories and industrial automation. Sensing and transmitting physical state measurements is the first step and the prerequisite for IIoT-based control. However, sensor interference (e.g., electromagnetic interference on sensing, temperature, and humidity variations in the field) and network interference (e.g., metal obstacles and background noises) may destroy the control performance by interfering with sensing and communication processes. Most of the present upstream “fixed sensors-networking-state estimation” approaches cannot effectively deal with sensor and network interferences due to the fixed measurements/estimation and network resource limitations. To optimize the performance of IIoT-based control, we propose a smart sensing and communication co-design (SSCC) framework to select more potential sensors and establish the corresponding network scheduling. SSCC consists of a smart estimator (SE) and a sensing communication mode switching (SCMS) agent. The SE detects sensor interference and obtains resilient state estimation based on collaborative sensing. SCMS agent dynamically switches sensor selections and network configurations (routing and transmission number) in an integrated manner based on the network and plant states by solving a performance optimization problem. We propose a lightweight SCMS approach by searching a predefined mode table. We perform simulations integrating TOSSIM and MATLAB/Simulink, and semi-physical experiments on a real wireless sensor-actuator network composed of TelosB nodes. The results show that the SSCC framework can effectively improve the control performance and enhance network energy efficiency under various types of interference by dynamically selecting sensors and allocating network resources.