基于集成学习和云-端集成的卫星网络人工智能服务

Zhen Gao, Yuqiu Zhang, Wenqiao Sun
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引用次数: 0

摘要

卫星网络是偏远地区无线覆盖对地面网络的必要补充。与此同时,基于深度神经网络(Deep Neural Networks, DNN)的人工智能(artificial intelligence, AI)应用已经广泛应用于人们生活的许多方面,但由于DNN模型的高复杂性,大多数DNN模型部署在云端。由于云距离偏远地区的终端用户较远,基于云的人工智能服务无法满足偏远地区用户的延迟需求。基于卫星的边缘计算是一个很有前途的解决方案,但卫星平台有限的资源无法支持复杂的模型。在本文中,我们提出将云、卫星边缘和终端用户整合形成一个人工智能服务框架,并应用集成学习提供高效可靠的人工智能服务。通过对图像分类任务的两个实例研究,验证了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Artificial Intelligence Service by Satellite Networks based on Ensemble Learning with Cloud-Edge-End Integration
Satellite network is the necessary supplement to ground network for wireless coverage for remote area. At the same time, Deep Neural Networks (DNN) based artificial intelligence (AI) applications has been widely applied in many aspects of people's life, but most DNN models are deployed in cloud due to their high complexity. Since cloud is far from end users in remote area, cloud based AI service cannot meet the latency requirement for users at remote area. Satellite based edge computing is a promising solution to this problem, but the limited resources on satellite platform cannot support complex models. In this paper, we propose to integrate the cloud, satellite edges and the end users to form an AI service framework, and apply ensemble learning to provide efficient and reliable AI service. Two case studies on image classification task are performed to show the effectiveness of the proposed scheme.
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