Reinforcement Learning Controlled Adaptive PSO for Task Offloading in IIoT Edge Computing

TL;DR

This paper introduces a hybrid reinforcement learning and adaptive particle swarm optimization approach to improve task offloading efficiency and resource management in IIoT edge computing environments.

Contribution

It presents a novel combination of SAC and APSO for dynamic, optimized task offloading in MEC, enhancing IIoT performance.

Findings

Improved task offloading efficiency in IIoT edge computing.

Enhanced resource utilization and service quality.

Adaptive decision-making in dynamic environments.

Abstract

Industrial Internet of Things (IIoT) applications demand efficient task offloading to handle heavy data loads with minimal latency. Mobile Edge Computing (MEC) brings computation closer to devices to reduce latency and server load, optimal performance requires advanced optimization techniques. We propose a novel solution combining Adaptive Particle Swarm Optimization (APSO) with Reinforcement Learning, specifically Soft Actor Critic (SAC), to enhance task offloading decisions in MEC environments. This hybrid approach leverages swarm intelligence and predictive models to adapt to dynamic variables such as human interactions and environmental changes. Our method improves resource management and service quality, achieving optimal task offloading and resource distribution in IIoT edge computing.