부산대학교 도서관

For practical industrial wireless sensor networks (IWSNs), the system freshness of a specific task is usually related to multiple and multitype sensing data. However, most existing research on freshness metrics, such as Age of Information (AoI) or Age of Processing (AoP), only considers a single-package setting with a single type of data. To fill this gap, we propose the Age of Task-oriented Information (AoTI) for measuring the freshness of industrial tasks in IWSNs. It measures the time elapsed of the latest analyzed results before arriving at the receiver since the generation of any type of sampling data belonging to one certain task. Furthermore, we aim to minimize the long-term AoTI for IWSNs applications by jointly optimizing access modes and sampling frequencies for all sensors. By first formulating the problem as a Mixed Integer Nonlinear Program-ming problem, we then transform it to a constrained Markov Decision Process (CMDP) and relax it as an un-constrained MDP using Lagrangian method. Finally, we develop a Learning-based Access mode selection and Sampling frequency Control (LASC) algorithm and verify its superiority through simulations.