海报:用于网络优化的深度学习M2M网关

MobiSys '16 Companion Pub Date : 2016-06-25 DOI:10.1145/2938559.2938592

Shivashankar Subramanian, Arindam Banerjee

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引用次数: 2

摘要

跨应用程序域，理想情况下M2M网关应该提供各种级别的网络优化。它的范围可以从延迟从网关到应用服务器的传输、从推送到拉模式的改变，到停止(重新)从设备到网关的传输。在这项工作中，我们提出了一个支持深度学习的M2M系统来处理异构数据源以进行网络优化。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Poster: Deep Learning Enabled M2M Gateway for Network Optimization

Across application domains, M2M gateway should ideally provide various levels of network optimization. It can range from delaying transmissions from gateway to application server, change from push to pull mode, to stopping (re)-transmissions from devices to gateway. In this work, we propose a deep learning enabled M2M system to handle heterogeneous data sources for network optimization.

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MobiSys '16 Companion

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