权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

WiFiUS: Collaborative Research: Secure Inference in the Internet of Things

WiFiUS：协作研究：物联网中的安全推理

基本信息

批准号：
1702808
负责人：
Harold Vincent Poor
金额：
$ 15万
依托单位：
Princeton University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2017
资助国家：
美国
起止时间：
2017-04-01 至 2019-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1702808&HistoricalAwards=false
关键词：
WiFiUS Collaborative Research Secure Inference

项目摘要

The next generation of communication networks is predicted to provide an Internet of Things (IoT) interconnecting up to 1 trillion sensors, products, machines, and devices by 2022. This research addresses the critical problem of security in these important emerging networks. Due to the limited capability of typical IoT terminals, the strict delay requirements of anticipated real-time IoT applications, and the broadcast nature of wireless channels, this project investigates physical layer and low complexity cryptographic methods to prevent eavesdroppers from gaining access to IoT data. IoT terminals will often communication via short messages, requiring new theory beyond the existing theory based on infinitely long messages. Since inference takes a central role in IoT systems, this research focuses on inference-based metrics. This project enables student training at the three participating universities, organization of workshops at major conferences, and development of course materials.Building on very recent work by the members of the project team, the team is developing bounds and approximations to fundamental performance indices in order to understand and design inferential networks based on short packet communications. To gain insight into large-scale inference networks, the team is investigating scaling laws for inferential performance which achieve optimum scaling behavior as one varies the ratio of the number of legitimate users to the number of eavesdroppers. New practical low-complexity cryptographic methods employing non-binary quantization, symbol flipping, channel state information and related approaches are under development and are expected provide significant advantages over existing methods of protecting IoT data. Distributed inference and learning methods that complement the developed approaches are also under development. Fast bootstrapping methods and low-complexity estimation, decision making and classification methods for distributed sensors in IoT are being derived as well.

预计到2022年，下一代通信网络将提供物联网（IoT），将多达1万亿个传感器，产品，机器和设备互连。这项研究解决了这些重要的新兴网络的安全性的关键问题。由于典型物联网终端的能力有限，预期实时物联网应用的严格延迟要求以及无线信道的广播性质，该项目研究了物理层和低复杂度的加密方法，以防止窃听者访问物联网数据。物联网终端通常通过短消息进行通信，需要新的理论来超越基于无限长消息的现有理论。由于推理在物联网系统中起着核心作用，因此本研究重点关注基于推理的度量。通过该项目，可以在3所大学进行学生培训、在大型会议上组织研讨会、制作课程材料。在项目组成员最近的工作基础上，为了理解和设计基于短分组通信的推理网络，正在开发基本性能指标的界限和近似值。为了深入了解大规模的推理网络，该团队正在研究推理性能的缩放律，当合法用户数量与窃听者数量的比例发生变化时，该缩放律可以实现最佳缩放行为。采用非二进制量化、符号翻转、信道状态信息和相关方法的新的实用低复杂度加密方法正在开发中，预计将比现有的保护物联网数据的方法具有显着优势。分布式推理和学习方法，补充发达国家的做法也正在开发中。物联网中分布式传感器的快速自举方法和低复杂度的估计，决策和分类方法也在不断发展。