基于相似度的边缘云协同人工智能数据传输减少方案

Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference Pub Date : 2022-12-17 DOI:10.1145/3582099.3582107

A. Elouali, H. Mora, Francisco J. Mora Gimeno

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引用次数: 0

摘要

物联网数据的边缘云协同处理是一种相对较新的方法，试图解决物联网系统中的处理和网络问题。它将神经网络模型所做的处理分为边缘部分和云部分，以解决网络、隐私和负载问题。然而，它也有缺点，比如边缘部分的输出尺寸大，必须传输到云端。在本文中，我们提出了一种基于静止物体数据相似度的边缘云协同解决方案的数据传输减少方法。实验证明，我们能够减少62%的数据发送。

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Similarity-based data transmission reduction solution for edge-cloud collaborative AI

Edge-cloud collaborative processing for IoT data is a relatively new approach that tries to solve processing and network issues in IoT systems. It consists of splitting the processing done by a Neural Network model into edge part and cloud part in order to solve network, privacy and load issues. However, it also has it shortcomings such as the big size of the edge part's output that has to be transmitted to the cloud. In this paper, we are proposing a data transmission reduction method for edge-cloud collaborative solutions that is based on data similarities in stationary objects. The performed experiments proved that we were able to reduce 62% of the data sent.

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Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference

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