Energy Conserved Failure Detection for NS-IoT Systems

TL;DR

This paper introduces an energy-efficient failure detection method for NS-IoT systems using dynamic dormancy of monitoring functions guided by reinforcement learning, significantly reducing resource consumption while maintaining detection effectiveness.

Contribution

It proposes a novel dynamic dormancy monitoring mechanism for NS-IoT systems based on NWDAF and reinforcement learning, optimizing energy use during failure detection.

Findings

The proposed strategy maximizes energy conservation in NS-IoT systems.

Reinforcement learning-based PPO algorithm outperforms alternatives in efficiency.

Simulation results confirm improved energy savings and system 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.