Graph-Based Proximal Policy Optimization Empowered Adaptive Task Scheduling Leveraging Cloud–Edge Collaboration for Consumer Electronics

The rapid advancement of IoT, AI, and edge computing has led to a significant increase in consumer devices and computation tasks, with new electronics incorporating these technologies to enhance services like VR/AR and autonomous driving, requiring real-time processing for safety and efficiency. However, recent research has focused on optimizing IoT task scheduling and resource allocation through various methods, yet overlooks the dynamic nature of IoT environments, fails to adapt to changing device counts and movements, and often ignores task completion rate in favor of minimizing latency and energy cost. In this context, an adaptive task scheduling and resource allocation strategy is proposed for Edge IoT systems, based on the designed Graph-based Proximal Policy Optimization (GPPO) algorithm. Firstly, the GPPO algorithm enhances PPO for adaptive task scheduling in the fluctuating MEC scenarios, adjusting for the varying number of nearby edge servers. Secondly, it accounts for consumer mobility by opting for local task execution if the consumer risks moving outside the edge server’s range, ensuring result reception. Thirdly, it prioritizes task completion rate to increase the number of tasks finished within their acceptable duration. Experimental results demonstrated that the proposed method outperforms traditional methods.