Efficient data transfer in edge envisioned environment using artificial intelligence based edge node algorithm

Abstract

AbstractEdge computing technology has drawn the keystone of future intelligent transportation systems, especially in smart cities, because of processing data that are near to the user location present at the edge of the cloud server. Generally, in smart cities where distributed things have access to computational resources, data transfer becomes inevitable because of high latency, thereby resulting in crucial situations. Even though numerous technologies have emerged for improving the data communication among geo‐distributed devices received from the cloud server but still, it lags in low learning performance. To address these challenges, an innovative artificial intelligence (AI) based edge node (E‐Node) algorithm is implemented to optimize the edge to edge learning for well‐organized data migration. To attain high reliability, AI‐K‐means neural network (KNN) and convolution neural network (CNN) is used initially for pre‐processing and filtering the edge node. Further, the proposed E‐Node algorithm outperforms the optimization technique effectively through the edge to edge computing The reliability performance is increased by reducing the node optimization time from 132 to 98 ms for 25 kb data and data transmission time is reduced from 93 to 45 ms for 80 kb data, thus reducing the latency in an edge envisioned environment.