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.

预计下一代通信网络将提供物联网（IoT）到2022年最多1万亿传感器，产品，机器和设备。这项研究解决了这些重要新兴网络中安全性的关键问题。由于典型的物联网终端的能力有限，预期的实时物联网应用的严格延迟要求以及无线通道的广播性质，该项目研究了物理层和低复杂性加密方法，以防止窃听者访问IoT数据。物联网终端通常会通过简短消息进行交流，需要基于无限长消息的现有理论以外的新理论。由于推论在物联网系统中扮演了核心角色，因此该研究重点是基于推理的指标。该项目使学生能够在三所参与大学的大学进行培训，主要会议上的研讨会以及课程材料的开发。在项目团队成员的最新工作中，该团队正在为基本绩效指数开发范围和近似值，以实现基本的绩效指数。理解和设计基于简短数据包通信的推论网络。为了深入了解大规模推理网络，该团队正在研究推理性能的缩放定律，以实现最佳缩放行为，因为一个人的数量与合法用户的数量与窃听者的数量不同。使用非二进制量化，符号翻转，渠道状态信息和相关方法的新实用的低复杂性加密方法正在开发中，并有望比现有保护IoT数据的方法具有显着优势。补充开发方法的分布推理和学习方法也正在开发中。快速引导方法和低复杂性估计，在物联网中分布式传感器的决策和分类方法也被得出。