CRII: CIF: Fundamental Research on Energy Efficiency and Security of RF Energy Harvesting Wireless Networks

CRII：CIF：射频能量收集无线网络的能源效率和安全性基础研究

• 批准号: 1948512
• 负责人: Lina Pu
• 金额: $ 11.49万
• 依托单位: University of Southern Mississippi
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2020-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1948512&HistoricalAwards=false
• 关键词: CRII; CIF; Fundamental; Research; Energy

To meet the ever-growing energy need for powering numerous wireless devices in applications of smart city, precision agriculture, and public safety, energy harvesting is emerging as a promising alternative to traditional power grids and batteries. Through scavenging radio energy from the surrounding environment, the radio frequency (RF) energy harvesting device can run semi-perpetually without a battery replacement. The energy harvesting feature, however, raises unique vulnerabilities in RF energy harvesting wireless networks. The investigator identifies a radically new threat model, called malicious energy attack: the adversary energy source threatens the information security by intentionally charging specific nodes to manipulate routing paths in the network layer. This project explores the non-linearity in energy harvesting systems and investigates reinforcement learning-enabled energy attack and defense mechanisms. It aims to develop efficient and secure communications for RF energy harvesting wireless networks. The project is expected to support development of sustainable and secure wireless networks, thereby leading to a broad impact on industrial sectors relying on energy harvesting techniques. The material generated from this project will be integrated into multiple courses on computer security and wireless networks. This project is organized around two research topics. The first topic develops offline and online optimal data transmission strategies based on a new nonlinear recursive energy harvesting model that considers the energy clamp phenomenon caused by dynamic RF environment, the nonlinear charging of the battery, and the nonlinear harvesting circuits. The second topic proposes a reinforcement learning-enabled intelligent energy attack method, where the adversary energy source can intelligently charge and control the energy level of each infected node based on the partial information of the traffic distribution in the network. Meanwhile, two defense mechanisms are developed to protect RF energy harvesting wireless networks from malicious energy attacks. In performing the above tasks, this research will build an RF energy harvesting wireless network testbed to facilitate radical breakthroughs over the practical design of models, algorithms, and protocols in forked projects.This project is jointly funded by CISE CCF and the Established Program to Stimulate Competitive Research (EPSCoR).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.

为了满足不断增长的能源需求，可以在智能城市，精密农业和公共安全的应用中为众多无线设备供电，能源收获正在成为传统电网和电池的一种有希望的替代方案。通过从周围环境中清除无线电能量，射频（RF）能量收集设备可以半完美地运行，而无需更换电池。然而，能源收集功能增加了RF能源收集无线网络的独特漏洞。研究人员确定了一个彻底的新威胁模型，称为恶意能量攻击：对手能源来通过有意收取特定节点来操纵网络层中的路由路径来威胁信息安全性。该项目探讨了能源收集系统中的非线性，并研究了增强学习支持的能源攻击和防御机制。它旨在为RF能源收集无线网络开发高效且安全的通信。该项目有望支持可持续和安全的无线网络的开发，从而导致对依靠能源收集技术的工业部门产生广泛的影响。该项目产生的材料将集成到有关计算机安全和无线网络的多个课程中。该项目围绕两个研究主题组织。第一个主题基于新的非线性递归能量收集模型，开发离线和在线最佳数据传输策略，该模型考虑了由动态RF环境，电池的非线性充电以及非线性收获电路引起的能量夹具现象。第二个主题提出了一种强化学习支持的智能能源攻击方法，在该方法中，对手能源可以根据网络中流量分布的部分信息来智能地为每个受感染节点的能量收取和控制每个受感染节点的能级。同时，开发了两种防御机制，以保护RF能源收集无线网络免受恶意攻击。在执行上述任务时，这项研究将建立RF能量收集无线网络测试，以促进对模型，算法和分叉项目中的实践设计的激进突破，该项目由CISE CCF共同资助，由CISE CCF共同资助，由CISE CCF和既定的计划进行了启发竞争性研究（EPSCOR）。智力优点和更广泛的影响审查标准。