A Multi-Agent Collaboration-Based Edge Data Processing and Decision-Making Mechanism for the Industrial Internet of Things

Abstract

The massive amount of data generated at the edge side of the Industrial Internet of Things imposes stringent requirements on processing real-time performance and decision-making intelligence, while traditional centralized architectures face latency and bandwidth bottlenecks. To address this challenge, a distributed processing framework based on multi-agent collaboration is constructed. Edge nodes are abstracted as autonomous decision-making entities through multi-agent modeling, a distributed consensus mechanism is adopted to achieve heterogeneous data synchronization, and an event-driven protocol is designed to optimize communication efficiency. A deep reinforcement learning model is introduced to enable online extraction of data features and dynamic task scheduling, and it is adapted to resource constraints through model lightweighting techniques. Furthermore, game theory and policy gradient methods are integrated to establish a collaborative decision-making mechanism in dynamic environments, ensuring the consistency of local strategies with global objectives. This study has constructed a complete technical system ranging from data interaction to intelligent decision-making, providing theoretical support for edge intelligence in the Industrial Internet of Things.