Real-time data processing optimization for industrial IoT enabled by edge computing

Abstract

With the rapid development of industrial Internet of Things (IoT) and real-time data processing technologies, edge computing, as an emerging computing paradigm, has garnered significant attention. This paper addresses the challenges of high bandwidth consumption and latency in traditional cloud computing networks by employing multi-source heterogeneous data collection, cloud-edge collaboration, and low-latency data transmission technologies to optimize real-time data processing efficiency. Through performance tests, transmission error rates, and packet loss rates, the experimental results show that the average response time, throughput, and CPU utilization rate are all below 32%. The overall error rate of edge computing is approximately 0.01133%. When the communication distance reaches 150 meters, the packet loss rate is only 0.041%. Therefore, the edge computing framework architecture in this paper demonstrates excellent communication capabilities, low packet loss rates, and high data transmission security.