DISCO:超轻型移动探索

S. Faye, F. Melakessou, D. Khadraoui
{"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}
引用次数: 2

摘要

捕获个人移动模式已成为大量应用程序的关键问题,通常需要使用侵犯隐私或消耗能量的传感器和在线服务。与此同时,无线网络接入点(ap)的扩散,以非常密集的方式分散在许多地理区域,现在正在开辟新的技术机会。在这项工作中,我们演示了使用从Wi-Fi ap被动收集的网络发现数据来推断移动性指标。这种本地方法可以在任何具有通信接口的设备上实现,并允许连续和长期的数据收集。该演示展示了一个多平台移动应用程序(DISCO),并与扩展的桌面分析工具箱一起呈现。更多的材料可以在网上找到。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
DISCO: Ultra-Lightweight Mobility Discovery
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信