Epsilon-Greedy-Based MQTT QoS Mode Selection and Power Control Algorithm for Power Distribution IoT

Abstract

Employing message queuing telemetry transport (MQTT) in the power distribution internet of things (PD-IoT) can meet the demands of reliable data transmission while significantly reducing energy consumption through the dynamic and flexible selection of three different quality of service (QoS) modes and power control. However, there are still some challenges, including incomplete information, coupling of optimization variables, and dynamic tradeoff between packet-loss ratio and energy consumption. In this paper, the authors propose a joint optimization algorithm named EMMA for MQTT QoS mode selection and power control based on the epsilon-greedy algorithm. Firstly, the joint optimization problem of MQTT QoS mode selection and power control is modeled as a multi-armed bandit (MAB) problem. Secondly, the authors leverage the online learning capability of the epsilon-greedy algorithm to achieve joint optimization of MQTT QoS mode selection and power control. Finally, they verify the superior performance of the proposed algorithm through simulations.