{"title":"设计一种基于隐马尔可夫模型的物联网电源感知方法","authors":"Palani Kumar, Meenakshi D'Souza","doi":"10.1109/COMSNETS.2017.7945458","DOIUrl":null,"url":null,"abstract":"Evolution of Internet of Things (IoT) demands interconnection of many autonomous and heterogeneous devices. Several such devices have very limited power. Every bit transmission consumes power and it is critical. The efficient power usage is a challenge. In this paper, we model an IoT device as a simple Hidden Markov Model (HMM) with a finite number of states and well determined emission probabilities. States in our HMM indicates the status of a device. We use the HMM to efficiently orchestrate the heartbeat duration of an IoT system. Our approach can identify the device anomaly with high accuracy and also save the end device power, by intelligently transmitting heartbeats based on HMM analysis. Our experimental result shows that, determination of device anomaly can be as high as 98%.","PeriodicalId":168357,"journal":{"name":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","volume":"30 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Design a power aware methodology in IoT based on Hidden Markov Model\",\"authors\":\"Palani Kumar, Meenakshi D'Souza\",\"doi\":\"10.1109/COMSNETS.2017.7945458\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Evolution of Internet of Things (IoT) demands interconnection of many autonomous and heterogeneous devices. Several such devices have very limited power. Every bit transmission consumes power and it is critical. The efficient power usage is a challenge. In this paper, we model an IoT device as a simple Hidden Markov Model (HMM) with a finite number of states and well determined emission probabilities. States in our HMM indicates the status of a device. We use the HMM to efficiently orchestrate the heartbeat duration of an IoT system. Our approach can identify the device anomaly with high accuracy and also save the end device power, by intelligently transmitting heartbeats based on HMM analysis. Our experimental result shows that, determination of device anomaly can be as high as 98%.\",\"PeriodicalId\":168357,\"journal\":{\"name\":\"2017 9th International Conference on Communication Systems and Networks (COMSNETS)\",\"volume\":\"30 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1900-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 9th International Conference on Communication Systems and Networks (COMSNETS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/COMSNETS.2017.7945458\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 9th International Conference on Communication Systems and Networks (COMSNETS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/COMSNETS.2017.7945458","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Design a power aware methodology in IoT based on Hidden Markov Model
Evolution of Internet of Things (IoT) demands interconnection of many autonomous and heterogeneous devices. Several such devices have very limited power. Every bit transmission consumes power and it is critical. The efficient power usage is a challenge. In this paper, we model an IoT device as a simple Hidden Markov Model (HMM) with a finite number of states and well determined emission probabilities. States in our HMM indicates the status of a device. We use the HMM to efficiently orchestrate the heartbeat duration of an IoT system. Our approach can identify the device anomaly with high accuracy and also save the end device power, by intelligently transmitting heartbeats based on HMM analysis. Our experimental result shows that, determination of device anomaly can be as high as 98%.