Iot Data Processing and Scheduling Based on Deep Reinforcement Learning

With the continuous integration of IoT technology and information technology, edge computing, as an emerging computing paradigm, makes full use of terminals to process and analyse real-time data. The explosion of Internet of Things (IoT) devices has created challenges for traditional cloud-based data processing models due to high latency and availability requirements. This paper proposes a new edge computation-based framework for iot data processing and scheduling using deep reinforcement learning. The system architecture incorporates distributed iot data access, real-time processing, and an intelligent scheduler based on Deep q networks (DQN). A large number of experiments show that compared with traditional scheduling methods, the average task completion time is reduced by 20% and resource utilization is increased by 15%. The unique integration of edge computing and deep reinforcement learning provides a flexible and efficient platform for low -latency iot applications. Key results obtained from testing the proposed system, such as reduced task completion time and increased resource utilization.

Automated summary

This paper proposes an edge computing and deep reinforcement learning-based framework for IoT data processing and scheduling. The experiments show that compared to traditional methods, this framework can significantly reduce task completion time and increase resource utilization.