Edge Selection Method for Updating Edge-AI Using Similarity of Discriminator to Observe Road Space

Abstract

Distributed Edge-AI is being introduced to the road management approach. To avoid a tight communication network, it is necessary for updating Edge-AI to build a collaborative network of edges that does not assemble all data from each edge. Thus, in this paper, a method of selecting another edge with valid data for updating Edge-AI based on difference in property is proposed. In the proposed method, a similarity score between edges using activation vectors which represent the characteristics of the Edge-AI is calculated. In addition, the edge with the lowest similarity score is selected as containing valid data. By utilizing the proposed method, we expect to update Edge-AI with generality despite using a small amount of data. In the last of this paper, we verify the effectiveness of the proposed method by the experiments of estimating the road surface condition, which is one of the road management approaches.