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

为了满足智能城市、精准农业和公共安全应用中众多无线设备不断增长的能源需求，能量收集正在成为传统电网和电池的一种有前途的替代品。通过从周围环境中收集无线电能量，射频（RF）能量收集装置可以半永久运行，而无需更换电池。然而，能量收集功能在射频能量收集无线网络中提出了独特的漏洞。研究者确定了一种全新的威胁模型，称为恶意能量攻击：对手的能量源通过故意向特定节点收费来操纵网络层中的路由路径，从而威胁信息安全。该项目探讨了能量收集系统中的非线性，并研究了强化学习支持的能量攻击和防御机制。它旨在为射频能量收集无线网络开发高效和安全的通信。该项目预计将支持可持续和安全无线网络的发展，从而对依赖能量收集技术的工业部门产生广泛影响。从这个项目中产生的材料将被整合到计算机安全和无线网络的多个课程中。这个项目是围绕两个研究课题组织的。第一个课题基于一种新的非线性递归能量收集模型，考虑了动态射频环境引起的能量钳位现象、电池的非线性充电和非线性收集电路，开发了离线和在线最优数据传输策略。第二个主题提出了一种基于强化学习的智能能量攻击方法，对手能量源可以根据网络中流量分布的部分信息，智能地充电并控制每个感染节点的能量水平。同时，开发了两种防御机制来保护射频能量收集无线网络免受恶意能量攻击。在执行上述任务时，本研究将建立一个射频能量收集无线网络测试平台，以促进分叉项目中模型，算法和协议的实际设计的根本性突破。本项目由CISE CCF和促进竞争性研究的既定计划（EPSCoR）共同资助。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.