Valentin Radu, P. Katsikouli, Rik Sarkar, M. Marina
{"title":"海报:我在室内还是室外?","authors":"Valentin Radu, P. Katsikouli, Rik Sarkar, M. Marina","doi":"10.1145/2639108.2642916","DOIUrl":null,"url":null,"abstract":"The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability. In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor. Using semi-supervised machine learning techniques, our method automatically learns characteristics of new environments and devices, thereby achieves detection accuracy of over 90% even in unfamiliar circumstances. Therefore, it easily outperforms existing indoor-outdoor detection techniques based on static algorithms, or relying on energy hungry and unreliable GPS.","PeriodicalId":331897,"journal":{"name":"Proceedings of the 20th annual international conference on Mobile computing and networking","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":"{\"title\":\"Poster: am i indoor or outdoor?\",\"authors\":\"Valentin Radu, P. Katsikouli, Rik Sarkar, M. Marina\",\"doi\":\"10.1145/2639108.2642916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability. In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor. Using semi-supervised machine learning techniques, our method automatically learns characteristics of new environments and devices, thereby achieves detection accuracy of over 90% even in unfamiliar circumstances. Therefore, it easily outperforms existing indoor-outdoor detection techniques based on static algorithms, or relying on energy hungry and unreliable GPS.\",\"PeriodicalId\":331897,\"journal\":{\"name\":\"Proceedings of the 20th annual international conference on Mobile computing and networking\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 20th annual international conference on Mobile computing and networking\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2639108.2642916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 20th annual international conference on Mobile computing and networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2639108.2642916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
The environmental context of a mobile device determines where/how it is used, which can be exploited for efficient operation and better usability. In this work we describe a general method using only the lightweight sensors on a smartphone to detect if a device is indoor or outdoor. Using semi-supervised machine learning techniques, our method automatically learns characteristics of new environments and devices, thereby achieves detection accuracy of over 90% even in unfamiliar circumstances. Therefore, it easily outperforms existing indoor-outdoor detection techniques based on static algorithms, or relying on energy hungry and unreliable GPS.