  Nowadays, network slicing (NS) technology has gained widespread adoption
within Internet of Things (IoT) systems to meet diverse customized
requirements. In the NS based IoT systems, the detection of equipment failures
necessitates comprehensive equipment monitoring, which leads to significant
resource utilization, particularly within large-scale IoT ecosystems. Thus, the
imperative task of reducing failure rates while optimizing monitoring costs has
emerged. In this paper, we propose a monitor application function (MAF) based
dynamic dormancy monitoring mechanism for the novel NS-IoT system, which is
based on a network data analysis function (NWDAF) framework defined in Rel-17.
Within the NS-IoT system, all nodes are organized into groups, and multiple
MAFs are deployed to monitor each group of nodes. We also propose a dormancy
monitor mechanism to mitigate the monitoring energy consumption by placing the
MAFs, which is monitoring non-failure devices, in a dormant state. We propose a
reinforcement learning based PPO algorithm to guide the dynamic dormancy of
MAFs. Simulation results demonstrate that our dynamic dormancy strategy
maximizes energy conservation, while proposed algorithm outperforms
alternatives in terms of efficiency and stability.
