The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. In the context of AIoT edge computing, where IoT devices collect data from diverse sources and send them for real-time processing at edge servers, existing message queue systems face challenges in adapting to changing system conditions, such as fluctuations in the number of devices, message size, and frequency. This necessitates the development of an approach that can effectively decouple message processing and handle workload variations in the AIoT computing environment. This study presents a distributed message system for AIoT edge computing, specifically designed to address the challenges associated with message ordering in such environments. The system incorporates a novel partition selection algorithm (PSA) to ensure message order, balance the load among broker clusters, and enhance the availability of subscribable messages from AIoT edge devices. Furthermore, this study proposes the distributed message system configuration optimization algorithm (DMSCO), based on DDPG, to optimize the performance of the distributed message system. Experimental evaluations demonstrate that, compared to the genetic algorithm and random searching, the DMSCO algorithm can provide a significant improvement in system throughput to meet the specific demands of high-concurrency AIoT edge computing applications.

The convergence of artificial intelligence and the Internet of Things (IoT) has made remarkable strides in the realm of industry. The IoT devices in this computing model gather data from various sources and send it to edge servers for real-time processing, which presents a challenge to the existing message queue systems in Artificial Intelligence of Things (AIoT) Edge computing. These systems lack the adaptability to respond to the current state of the system, such as changes in the number of devices, message size, and frequency, and optimize the message transmission mechanism accordingly. Hence, it is critical to devise an approach that can effectively decouple message processing and mitigate workload fluctuations in the AIoT computing environment. To this end, this study introduces a distributed message queue system that is specifically tailored to the AIoT edge environment, utilizing a reinforcement learning approach to optimize message queue performance. Empirical findings reveal that this pioneering method significantly improves system throughput while handling varying message scenarios.