{"title":"DISCO:超轻型移动探索","authors":"S. Faye, F. Melakessou, D. Khadraoui","doi":"10.1145/3274783.3275173","DOIUrl":null,"url":null,"abstract":"Capturing individual mobility patterns has become a crucial issue for a tremendous number of applications, often requiring the use of privacy-invasive or energy-consuming sensors and online services. In parallel to this, the proliferation of wireless network access points (APs), scattered in a very dense manner in many geographical areas, is now opening up new technological opportunities. In this work, we demonstrate the use of network discovery data passively collected from Wi-Fi APs to infer mobility indicators. This local approach can potentially be implemented on any device with a communication interface, and allows for continuous and long-term data collection. The demo showcases a multi-platform mobile app (DISCO) and is presented alongside an extended desktop analysis toolbox. Additional material can be found online1.","PeriodicalId":156307,"journal":{"name":"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems","volume":"93 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"DISCO: Ultra-Lightweight Mobility Discovery\",\"authors\":\"S. Faye, F. Melakessou, D. Khadraoui\",\"doi\":\"10.1145/3274783.3275173\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Capturing individual mobility patterns has become a crucial issue for a tremendous number of applications, often requiring the use of privacy-invasive or energy-consuming sensors and online services. In parallel to this, the proliferation of wireless network access points (APs), scattered in a very dense manner in many geographical areas, is now opening up new technological opportunities. In this work, we demonstrate the use of network discovery data passively collected from Wi-Fi APs to infer mobility indicators. This local approach can potentially be implemented on any device with a communication interface, and allows for continuous and long-term data collection. The demo showcases a multi-platform mobile app (DISCO) and is presented alongside an extended desktop analysis toolbox. Additional material can be found online1.\",\"PeriodicalId\":156307,\"journal\":{\"name\":\"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems\",\"volume\":\"93 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-11-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 16th ACM Conference on Embedded Networked Sensor Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3274783.3275173\",\"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 16th ACM Conference on Embedded Networked Sensor Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3274783.3275173","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Capturing individual mobility patterns has become a crucial issue for a tremendous number of applications, often requiring the use of privacy-invasive or energy-consuming sensors and online services. In parallel to this, the proliferation of wireless network access points (APs), scattered in a very dense manner in many geographical areas, is now opening up new technological opportunities. In this work, we demonstrate the use of network discovery data passively collected from Wi-Fi APs to infer mobility indicators. This local approach can potentially be implemented on any device with a communication interface, and allows for continuous and long-term data collection. The demo showcases a multi-platform mobile app (DISCO) and is presented alongside an extended desktop analysis toolbox. Additional material can be found online1.