Research Initiation Award: Secure Spectrum Sharing for Intelligent Internet-of-Things (IoT) Wireless Communication Networks

研究启动奖为传统黑人学院和大学的初级和中期职业教师提供支持，他们正在建立新的研究项目或重新指导和重建现有的研究项目。期望该奖项有助于提高教师的研究能力和效率，改善所在机构的研究和教学，并使本科生参与研究经验。该奖项授予摩根州立大学，以支持开发保护智能物联网（IoT）设备的系统的研究，该项目支持“未来NSF投资的十大理念”之一，即“在人类技术前沿工作”。该项目的目标是通过设计和开发与许可用户共享频谱的方案来保护物联网用户的通信，同时绕过攻击者，从而最大限度地提高网络容量。首先，通过设计基于对抗性多臂强盗和随机时变反馈图的最优攻击策略，研究物联网无线通信网络的漏洞。在防御设计范例中，智能物联网设备将应用新型对抗性多臂强盗方法来进行有效的频率信道选择和数据传输，同时最大限度地减少信道切换延迟。此外，通道和功率分配的目标将整合到一个统一的框架中，以优化物联网用户的功耗。将无线信道的固有条件描述为时变随机过程，并考虑攻击者和物联网用户都是基于学习的，两个智能体形成一个在线重复随机博弈。它的目的是确定任何可能的平衡在这两个代理使用随机优化方法。此外，使用基于统计因素分析方法的数据驱动方法，物联网设备的鲁棒性将被表征为其智能因素的函数。然后，提出的防御框架将扩展到集中式和分散式多用户物联网网络，其中组合在线学习方法和各种碰撞解决技术将分别在集中式和分散式设置中使用。最终，将通过提取群体智能并检查其对对抗环境中物联网用户的弹性和网络容量的影响，定量评估多个物联网用户的集体智能。所提出的基于多学科技术的最优频谱接入策略的成功实施，将为物联网用户提供安全的频谱共享机制，并结合实际考虑，使他们能够绕过对手并最大化网络容量。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。

Research Initiation Awards provide support for junior and mid-career faculty at Historically Black Colleges and Universities who are building new research programs or redirecting and rebuilding existing research programs. It is expected that the award helps to further the faculty member's research capability and effectiveness, improves research and teaching at the home institution, and involves undergraduate students in research experiences. The award to Morgan State University supports research on developing systems that defend Intelligent Internet of Things (IoT) devices – a project that supports one of “The 10 Big Ideas for Future NSF Investments,” i.e. “Work at the Human-Technology Frontier." The goal of this project is to secure the IoT users' communication by designing and developing schemes to share the spectrum with licensed users while circumventing the attackers, thereby maximizing the network capacity. First, vulnerabilities of IoT wireless communication networks will be investigated by designing optimal attacking strategies based on adversarial multi-armed bandits, and randomized time-varying feedback graphs. In the defense design paradigm, an intelligent IoT device will apply novel adversarial multi-armed bandit methods for effective frequency channel selection and data transmission while minimizing channel switching delay. In addition, both objectives of channel and power allocation will be integrated into a unified framework to optimize the IoT user's power consumption. Characterizing the wireless channels' inherent conditions with a time-varying stochastic process and considering both the attacker and the IoT user to be learning-based, the two agents form an online repeated stochastic game. It is intended to identify any possible equilibrium between these two agents using stochastic optimization methods. Furthermore, using a data-driven methodology based on statistical factor analysis approaches, the IoT device's robustness will be characterized as a function of its intelligence factors. Then, the proposed defense frameworks will be extended to centralized and decentralized multi-user IoT networks where combinatorial online learning approaches and various collision resolutions techniques will be utilized in the centralized and decentralized settings, respectively. Eventually, the collective intelligence of multiple IoT users will be quantitatively evaluated by extracting the group intelligence and examining its impact on the IoT users’ resiliency and network capacity in adversarial settings. Successful implementation of the proposed optimal spectrum access policy based on multidisciplinary techniques will provide secure spectrum sharing mechanisms for the IoT users with practical considerations which enables them to circumvent the adversary and maximize the network capacity.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.