Intelligent Transmission Scheduling for Edge Sensing in Industrial IoT Systems

Edge sensing supported by wireless transmission is one of the core enabling technologies for flexibly implementing the Industrial Internet of Things (IIoT). Balancing network resource consumption and sensing accuracy under dynamic network conditions is a critical challenge. In this work, we bridge the gap between edge sensing performance and transmission design through observability analysis and learning-based methods. Particularly, utilizing observability probability as the key metric, we design the network resource reservation for specific sensing performance demands including stability based on our derived upper and lower probability bounds. Then, to further reduce the overall cost of edge sensing and transmission, an intelligent transmission scheduling method (ITSM) based on deep reinforcement learning is provided, which dynamically schedules the number of transmissions for each sensor. In ITSM, the action space is determined according to the amount of our reserved resources, and both the states of sensing error and fading channel are taken into account. Finally, the superiority of our proposed methods is fully demonstrated through numerical simulations in a typical IIoT system of industrial hot rolling.