Autonomous Maintenance in IoT Networks via AoI-driven Deep Reinforcement Learning

TL;DR

This paper introduces a novel approach for autonomous maintenance in IoT networks using deep reinforcement learning guided by the Age of Information metric, effectively managing network resources and ensuring data freshness.

Contribution

It formulates the maintenance problem as a POMDP and employs AoI as a reward signal for training DRL agents, which is a new application in IoT network management.

Findings

AoI effectively guides DRL agents for maintenance decisions.

The approach improves data freshness and resource utilization.

Numerical results validate the method's effectiveness.

Abstract

Internet of Things (IoT) with its growing number of deployed devices and applications raises significant challenges for network maintenance procedures. In this work, we formulate a problem of autonomous maintenance in IoT networks as a Partially Observable Markov Decision Process. Subsequently, we utilize Deep Reinforcement Learning algorithms (DRL) to train agents that decide if a maintenance procedure is in order or not and, in the former case, the proper type of maintenance needed. To avoid wasting the scarce resources of IoT networks we utilize the Age of Information (AoI) metric as a reward signal for the training of the smart agents. AoI captures the freshness of the sensory data which are transmitted by the IoT sensors as part of their normal service provision. Numerical results indicate that AoI integrates enough information about the past and present states of the system to be successfully used in the training of smart agents for the autonomous maintenance of the network.