Enhanced Pub/Sub Communications for Massive IoT Traffic with SARSA Reinforcement Learning

TL;DR

This paper proposes a SARSA-based reinforcement learning approach to optimize bandwidth allocation in a publish/subscribe IoT network, improving data transmission efficiency under resource constraints.

Contribution

It introduces a novel RL-based bandwidth management scheme for IoT networks using publish/subscribe architecture, enhancing resource utilization and data delivery.

Findings

Improved network link utilization with SARSA-based allocation

Enhanced IoT aggregator buffer management

Dynamic adaptation to network constraints

Abstract

Sensors are being extensively deployed and are expected to expand at significant rates in the coming years. They typically generate a large volume of data on the internet of things (IoT) application areas like smart cities, intelligent traffic systems, smart grid, and e-health. Cloud, edge and fog computing are potential and competitive strategies for collecting, processing, and distributing IoT data. However, cloud, edge, and fog-based solutions need to tackle the distribution of a high volume of IoT data efficiently through constrained and limited resource network infrastructures. This paper addresses the issue of conveying a massive volume of IoT data through a network with limited communications resources (bandwidth) using a cognitive communications resource allocation based on Reinforcement Learning (RL) with SARSA algorithm. The proposed network infrastructure (PSIoTRL) uses a Publish/ Subscribe architecture to access massive and highly distributed IoT data. It is demonstrated that the PSIoTRL bandwidth allocation for buffer flushing based on SARSA enhances the IoT aggregator buffer occupation and network link utilization. The PSIoTRL dynamically adapts the IoT aggregator traffic flushing according to the Pub/Sub topic's priority and network constraint requirements.