SpecEES: Towards Secure Decision Making in Spectrum and Energy Efficient IoT Systems

SpecEES：在频谱和节能物联网系统中实现安全决策

• 批准号: 1824553
• 负责人: Zhi Ding
• 金额: $ 55万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1824553&HistoricalAwards=false
• 关键词: SpecEES; Towards; Secure; Decision; Making

The Internet-of-Things (IoT) has recently emerged as a powerful new paradigm for future generations of wireless networks. In IoT, a wide variety of devices, such as body sensors, implants, animal biochip transponders, electric clams in coastal waters, vehicular sensors, sensors for environmental/food/pathogen monitoring, and devices in disaster relief operations, are connected to the Internet via wireless interfaces. Connecting this myriad of mobile devices to the Internet could potentially lead to a broad range of innovative network applications. However, unique technical challenges for IoT, such as massive connectivity, security vulnerability and energy sustainability, among others, need to be addressed before such potentials can be fully fulfilled. This project aims to develop a robust and secure framework for wireless function computing to specifically address challenges in IoT applications. The enabling characteristic of this framework is that in many IoT applications, instead of recovering the full data collected by various devices and transmitted over the network, the goal of the networked functional computing is to assist certain decision making, for which the decision makers only need to compute a function of these distributed data. The main idea of this project is to develop secure and spectrum/energy efficient protocols that enable the decision maker to compute functions of interest without first recovering the full data from sensing devices. Thus, instead of acting as mere data pipes, wireless links become an integral part of the smart decision process in IoT applications. Successful execution of this project will make substantial contributions to both practical applications and theory development. On the application level, this project has the potential to substantially improve the spectrum efficiency, energy efficiency and robustness to active attacks for future IoT networks. On the theory side, this project will develop low-complexity efficient and robust function computing algorithms that enable IoT applications to make timely and accurate decisions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

物联网(IoT)最近已成为未来几代无线网络的强大新范式。在物联网中，各种各样的设备，如身体传感器、植入物、动物生物芯片应答器、沿海水域的电蚌、车辆传感器、环境/食物/病原体监测传感器，以及救灾行动中的设备，都通过无线接口连接到互联网。将这无数的移动设备连接到互联网可能会带来广泛的创新网络应用。然而，物联网面临的独特技术挑战，如大规模连接、安全漏洞和能源可持续性等，需要在充分发挥这些潜力之前得到解决。该项目旨在为无线功能计算开发一个强大且安全的框架，以专门应对物联网应用中的挑战。该框架的使能特征是，在许多物联网应用中，联网功能计算的目标不是恢复由各种设备收集并通过网络传输的全部数据，而是帮助特定的决策，决策者只需计算这些分布式数据的函数。该项目的主要思想是开发安全和频谱/能源效率高的协议，使决策者能够计算感兴趣的函数，而无需首先从传感设备恢复完整数据。因此，无线链路不再仅仅充当数据管道，而是成为物联网应用中智能决策过程的组成部分。该项目的成功实施将对实际应用和理论发展做出重大贡献。在应用层面上，该项目有可能大幅提高未来物联网网络的频谱效率、能源效率和对主动攻击的健壮性。在理论方面，该项目将开发低复杂性、高效和健壮的函数计算算法，使物联网应用程序能够做出及时和准确的决策。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Topology Inference of Unknown Networks Based on Robust Virtual Coordinate Systems

• DOI: 10.1109/tnet.2018.2888600
• 发表时间: 2019-02
• 期刊: IEEE/ACM Transactions on Networking
• 作者: Taha Bouchoucha;Chen-Nee Chuah;Z. Ding

Analysis of KNN Information Estimators for Smooth Distributions

平滑分布的KNN信息估计器分析

• DOI: 10.1109/tit.2019.2945041
• 发表时间: 2020
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Zhao, Puning;Lai, Lifeng
• 通讯作者: Lai, Lifeng

Distributed Approximate Newton's Method Robust to Byzantine Attackers

• DOI: 10.1109/tsp.2020.3029461
• 发表时间: 2020
• 期刊: IEEE Transactions on Signal Processing
• 影响因子: 5.4
• 作者: Xinyang Cao;L. Lai

Optimal Two-Stage Bayesian Sequential Change Diagnosis

• DOI: 10.1109/isit44484.2020.9173938
• 发表时间: 2020-06
• 期刊: 2020 IEEE International Symposium on Information Theory (ISIT)
• 作者: Xiaochuan Ma;L. Lai;Shuguang Cui

Adversarially Robust Hypothesis Testing

对抗性稳健假设检验

• DOI: 10.1109/ieeeconf44664.2019.9048771
• 发表时间: 2019
• 期刊: and Computers
• 作者: Jin, Yulu;Lai, Lifeng
• 通讯作者: Lai, Lifeng