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.

翻译：当前，网络切片技术已在物联网系统中被广泛采用，以满足多样化的定制需求。在基于网络切片的物联网系统中，设备故障检测需要对设备进行全面监控，这会导致大量资源消耗，尤其是在大规模物联网生态系统中。因此，如何在降低故障率的同时优化监控成本已成为一项紧迫任务。本文针对新型网络切片物联网系统，提出了一种基于监控应用功能模块的动态休眠监控机制，该机制基于Rel-17标准中定义的网络数据分析功能框架。在网络切片物联网系统中，所有节点被组织成多个组，并部署多个监控应用功能模块对每组节点进行监控。我们还提出了一种休眠监控机制，通过将监控无故障设备的监控应用功能模块置于休眠状态来降低监控能耗。我们设计了一种基于强化学习的PPO算法来指导监控应用功能模块的动态休眠。仿真结果表明，我们的动态休眠策略能够最大化能量节约，同时所提出的算法在效率和稳定性方面优于其他方案。