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为众多无线设备供电的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）能量收集设备可以半永久地运行而无需更换电池。然而，能量收集特征在RF能量收集无线网络中引起独特的脆弱性。研究人员发现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RF能量收集无线网络开发高效和安全的通信。预计该项目将支持可持续和安全的无线网络的发展，从而对依赖能源收集技术的工业部门产生广泛影响。从这个项目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。该项目围绕两个研究主题展开。第一个主题开发离线和在线的最佳数据传输策略的基础上，一个新的非线性递归能量收集模型，考虑能量钳现象所造成的动态RF环境，电池的非线性充电，和非线性收集电路。第二个主题提出了一种支持强化学习的智能能量攻击方法，其中对手能量源可以根据网络中流量分布的部分信息智能地对每个感染节点的能量水平进行计费和控制。同时，两种防御机制，以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RF能量收集无线网络测试平台，以促进在模型，算法，该项目由CISE CCF和刺激竞争研究的既定计划（EPSCoR）共同资助。该奖项反映了NSF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.