An Edge Computing Node Deployment Method Based on Improved k-Means Clustering Algorithm for Smart Manufacturing

Abstract

With the rapid development of the mobile Internet, Industrial Internet of Things, cyber-physical systems, and the emergence of edge computing has provided an opportunity to realize the high computing performance and low latency of intelligent devices in the smart manufacturing environment. In this paper, we propose and verify an edge computing node deployment method for smart manufacturing. First, the architecture of a smart manufacturing system used for implementing the edge computing node deployment methods is presented. Then, comprehensively balancing the network delay and computing resources deployment cost, and considering the influence of device spatial distribution, device function, and computing capacity of edge nodes on the above optimization objectives, the optimal deployment number of edge computing nodes is obtained by using an improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering algorithm. Finally, a prototype platform is developed to verify the proposed method experimentally, and compare the improved <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering deployment method, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</i> -means clustering deployment method, and random deployment method. The proposed method is superior to the other two methods regarding both network delay and computing resources deployment cost. The experimental results show that the proposed edge computing node deployment method can be easily applied to the intelligent manufacturing system; also, the effectiveness and efficiency of this method are verified.