{"title":"物联网服务的细粒度冲突检测","authors":"Dipankar Chaki, A. Bouguettaya","doi":"10.1109/SCC49832.2020.00049","DOIUrl":null,"url":null,"abstract":"We propose a novel conflict detection framework for IoT services in multi-resident smart homes. A fine-grained conflict model is developed considering the functional and non-functional properties of IoT services. The proposed conflict model is designed using the concept of entropy and information gain from information theory. We use a novel algorithm based on temporal proximity to detect conflicts. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.","PeriodicalId":274909,"journal":{"name":"2020 IEEE International Conference on Services Computing (SCC)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Fine-grained Conflict Detection of IoT Services\",\"authors\":\"Dipankar Chaki, A. Bouguettaya\",\"doi\":\"10.1109/SCC49832.2020.00049\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose a novel conflict detection framework for IoT services in multi-resident smart homes. A fine-grained conflict model is developed considering the functional and non-functional properties of IoT services. The proposed conflict model is designed using the concept of entropy and information gain from information theory. We use a novel algorithm based on temporal proximity to detect conflicts. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.\",\"PeriodicalId\":274909,\"journal\":{\"name\":\"2020 IEEE International Conference on Services Computing (SCC)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-07-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE International Conference on Services Computing (SCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SCC49832.2020.00049\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE International Conference on Services Computing (SCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SCC49832.2020.00049","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose a novel conflict detection framework for IoT services in multi-resident smart homes. A fine-grained conflict model is developed considering the functional and non-functional properties of IoT services. The proposed conflict model is designed using the concept of entropy and information gain from information theory. We use a novel algorithm based on temporal proximity to detect conflicts. A set of experiments on real-world datasets are conducted to show the efficiency of the proposed approach.