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

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。实际上，期望开发利用现有无线基础设施和感测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。然而，这些无线数据是动态的，具有复杂的数据特性，如多尺度，多源和多模态。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的和可扩展的多模态被动入侵学习技术，通过自适应学习的协作方式，在普遍的无线环境中检测和跟踪无设备的移动对象的能力。与传统技术相比，传统技术需要预先部署专门的硬件，因此不容易部署用于计划外的任务，并且可能无法扩展，该项目通过挖掘无线环境数据，对入侵学习产生了新的见解，并导致了无设备无线定位的新方法，可用于协助广泛的应用程序（例如，在紧急疏散过程中识别被困在着火建筑物中的人员）。预计项目成果将为将学习能力纳入新兴的普遍无线领域开辟一个新的场所。教育部分力求使学生具备必要的背景知识和实用技能，为信息技术作出贡献，并对一系列跨部门领域产生实际影响。

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.