{"title":"Poster: Deep Learning Enabled M2M Gateway for Network Optimization","authors":"Shivashankar Subramanian, Arindam Banerjee","doi":"10.1145/2938559.2938592","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":298684,"journal":{"name":"MobiSys '16 Companion","volume":"167 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MobiSys '16 Companion","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2938559.2938592","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
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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