{"title":"物联网环境下基于图的M2M优化","authors":"Anand Paul","doi":"10.1145/2513228.2513287","DOIUrl":null,"url":null,"abstract":"In this paper, a graph based M2M optimization in an IoT environment is presented. A parallel reconfigurable M2M architecture with multiple dynamic reconfigurable unit in an IoT scenario is modeled using a directed acyclic graph (DAG) to represent the whole environment. Parallel M2M establish communication within the network and are partitioned and reconfigured dynamically for large scale network such as IoT. Simulation were performed for multiple M2M array for different state, timing and power consumption along with the scheduling scheme are considered.","PeriodicalId":120340,"journal":{"name":"Research in Adaptive and Convergent Systems","volume":"7 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"36","resultStr":"{\"title\":\"Graph based M2M optimization in an IoT environment\",\"authors\":\"Anand Paul\",\"doi\":\"10.1145/2513228.2513287\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, a graph based M2M optimization in an IoT environment is presented. A parallel reconfigurable M2M architecture with multiple dynamic reconfigurable unit in an IoT scenario is modeled using a directed acyclic graph (DAG) to represent the whole environment. Parallel M2M establish communication within the network and are partitioned and reconfigured dynamically for large scale network such as IoT. Simulation were performed for multiple M2M array for different state, timing and power consumption along with the scheduling scheme are considered.\",\"PeriodicalId\":120340,\"journal\":{\"name\":\"Research in Adaptive and Convergent Systems\",\"volume\":\"7 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"36\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Research in Adaptive and Convergent Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2513228.2513287\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Research in Adaptive and Convergent Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2513228.2513287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Graph based M2M optimization in an IoT environment
In this paper, a graph based M2M optimization in an IoT environment is presented. A parallel reconfigurable M2M architecture with multiple dynamic reconfigurable unit in an IoT scenario is modeled using a directed acyclic graph (DAG) to represent the whole environment. Parallel M2M establish communication within the network and are partitioned and reconfigured dynamically for large scale network such as IoT. Simulation were performed for multiple M2M array for different state, timing and power consumption along with the scheduling scheme are considered.