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: 1018151
• 负责人: Hui Xiong
• 金额: $ 24.69万
• 依托单位: Rutgers University Newark
• 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=1018151&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为影响我们的日常生活创造了前所未有的机会。无论无线基础设施是仅用于通信还是作为实际响应的基础，大规模无线数据都为检测移动物体引起的环境变化提供了越来越多的机会。事实上，预计将开发利用现有无线基础设施和传感数据来跟踪不携带无线电设备、甚至可能不知道被跟踪的移动物体的能力。然而，这些无线数据是动态的，具有多尺度、多源、多模态等复杂的数据特征。随着这些数据变得越来越大、越来越详细，入侵学习面临着新的挑战。该项目旨在开发有效且可扩展的多模式被动入侵学习技术，该技术能够通过协作方式的自适应学习来检测和跟踪普遍无线环境中的无设备移动物体。传统技术需要预先部署专用硬件，因此不容易部署用于计划外任务，并且可能无法扩展，而该项目通过挖掘无线环境数据带来了对入侵学习的新见解，并带来了无设备无线定位的新方法，可用于协助广泛的应用（例如，在紧急情况下识别被困在火灾建筑中的人员） 疏散）。项目结果预计将为将学习能力整合到新兴的普遍无线领域开辟一个新的场所。教育部分旨在为学生提供必要的背景和实践技能，为信息技术做出贡献并对大量跨领域领域产生实际影响。