Energy Conserved Failure Detection for NS-IoT Systems
Guojin Liu, Jianhong Zhou, Hang Su, Biaohong Xiong, Xianhua Niu
TL;DR
NS-IoT systems face high energy costs for continuous failure detection. The paper introduces a dynamic dormancy mechanism for Monitoring Application Functions (MAFs) guided by a PPO-based reinforcement learning policy, integrated within the NWDAF framework to optimize monitoring duration and sleep intervals. The problem is formulated as an MDP with states representing abnormality durations and rewards inversely tied to total energy, solved via PPO-Clip. Results show the PPO-based strategy significantly reduces energy consumption while meeting a 93% monitoring accuracy, outperforming DDPG and DQN in convergence and stability.
Abstract
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.