Abstract Efficient task scheduling in Cloud–Fog computing environments remains a critical challenge in Industrial Internet of Things (IIoT) systems due to the conflicting requirements of energy efficiency, latency reduction, and Quality of Service (QoS) assurance. This paper proposes an Energy-Aware Improved Artificial Bee Colony (EA-IABC) algorithm integrated with a Q-learning-based reinforcement learning (RL) module for multi-objective task scheduling in Green IIoT infrastructures.
