NeTSE:Small:Collaborative Research: MILAN: Multi-Modal Passive Intrusion Learning in Pervasive Wireless Environments

NeTSE:Small:协作研究：米兰：普遍无线环境中的多模式被动入侵学习

• 批准号: 1018270
• 负责人: Yingying Chen
• 金额: $ 24.8万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018270&HistoricalAwards=false
• 关键词: NeTSE; Small; Collaborative; Research; MILAN

The widespread deployment of wireless communication systems creates unprecedented opportunities to impact our daily lives. Regardless of whether wireless infrastructures are used just for communication or as the basis for actual responses, large-scale wireless data provide increasing opportunities for detecting environmental changes caused by moving objects. Indeed, it is expected to develop the ability to make use of existing wireless infrastructure and sensing data to track moving objects which do not carry radio devices and may not even being aware of being tracked. However, these wireless data are dynamic and have complex data characteristics, such as multi-scale, multi-source and multi-modal. As these data become large and more detailed, new challenges are emerging for intrusion learning.This project aims to develop effective and scalable multi-modal passive intrusion learning techniques that have the capability to detect and track device-free moving objects in pervasive wireless environments through adaptive learning in a collaborative way. In contrast to traditional techniques, which require pre-deployment of specialized hardware, and thus not easily deployed for unscheduled tasks and may not be scalable, this project leads to new insights into intrusion learning by mining on wireless environmental data, as well as leading to new approaches to device-free wireless localization, which can be used to assist a broad array of applications (e.g., identification of people trapped in a fire building during emergency evacuation). Project results are expected to open a new venue for integrating learning capabilities into emerging pervasive wireless fields. The educational component seeks to equip students with the necessary background and practical skills needed to contribute to information technology and have a practical impact on a large set of cross-section domains.

无线通信系统的广泛部署创造了前所未有的机会来影响我们的日常生活。无论无线基础架构是否仅用于通信还是实际响应的基础，大规模无线数据都为检测移动对象引起的环境变化提供了越来越多的机会。确实，预计将发展使用现有的无线基础架构和传感数据来跟踪不携带无线电设备并且甚至可能不知道被跟踪的移动对象的能力。但是，这些无线数据是动态的，具有复杂的数据特性，例如多尺度，多源和多模式。随着这些数据变得越来越详细，新的挑战正在出现在入侵学习中。该项目旨在开发有效且可扩展的多模式的被动入侵学习技术，这些学习技术具有通过协作方式适应性学习的无线无线环境中检测和跟踪无设备的移动对象的能力。与传统技术相反，这些技术需要预先放置专门的硬件，因此不容易部署用于未定规定的任务并且可能不可伸缩，该项目通过挖掘无线环境数据来实现对入侵学习的新见解，并导致无线局部的新方法，可以在范围内进行无效的范围，以帮助某种范围的人识别，以帮助范围内的范围，以识别一定的范围（E.G）（E.G（E.G）（E.G）（E. e. e.g）（E. e. e. e. gray of。撤离）。预计项目结果将为将学习能力整合到新兴的无线无线领域中开设一个新的场所。教育组成部分旨在为学生提供为信息技术做出贡献所需的必要背景和实践技能，并对一系列横截面领域产生实际影响。