查询结果:   苟英,李冀明,魏星.边缘计算的物联网深度学习及任务卸载调度策略[J].计算机应用与软件,2019,36(8):125 - 129.
中文标题
边缘计算的物联网深度学习及任务卸载调度策略
发表栏目
网络与通信
摘要点击数
675
英文标题
DEEP LEARNING OF IOT AND TASK UNLOADING SCHEDULING STRATEGY FOR EDGE COMPUTING
作 者
苟英 李冀明 魏星 Gou Ying Li Jiming Wei Xing
作者单位
重庆商务职业学院出版传媒系 重庆 401331 重庆工程学院软件学院 重庆 400056 重庆工程学院计算机学院 重庆 400056   
英文单位
Department of Publishing and Media, Chongqing Business Vocational College, Chongqing 401331,China School of Software,Chongqing Institute of Engineering, Chongqing 400056,China School of Computer,Chongqing Institute of Engineering, Chongqing 400056,China   
关键词
边缘计算 物联网 深度学习 卸载调度策略 用户隐私
Keywords
Edge computing IoT Deep learning Unloading scheduling policy User privacy
基金项目
重庆科技厅科技攻关项目(cstc2016jcyjA0469)
作者资料
苟英,副教授,主研领域:大数据,软件工程。李冀明,讲师。魏星,高工。 。
文章摘要
为解决物联网深度学习模型的网络性能和隐私问题,提出一种边缘计算的物联网深度学习应用及任务卸载策略,以优化网络性能,保护数据上传中的用户隐私。深度学习的多层结构适用于边缘计算,边缘节点上传缩减的中间数据,因此减少了从物联网设备到云服务器的网络流量。考虑到边缘节点有限的服务能力,提出一种边缘计算环境中最大化任务数量的卸载调度策略,优化边缘计算的物联网深度应用性能。实验结果表明,该策略能够在边缘计算环境中执行多个深度学习任务,并且性能优于其他物联网深度学习优化解决方案。
Abstract
To solve the network performance and privacy problems of IoT in deep learning model, we proposed an edge computing deep learning application and task uninstallation strategy of the IoT to optimize network performance and protect user privacy in data upload. The multi-layer structure of deep learning is suitable for edge computing, and edge nodes upload reduced intermediate data, thus reducing network traffic from IoT devices to cloud servers. Considering the limited service capability of edge nodes, we proposed an unloading scheduling strategy to maximize the number of tasks in edge computing environment to optimize the deep application performance of edge computing in the IoT. The experimental results show that the strategy can perform multiple deep learning tasks in the edge computing environment, and its performance is superior to other deep learning optimization solutions of the IoT.
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