EAGER: Biomimetic wireless system design for IoT networks: from sensors to brain controlled applications

物联网（IoT）将用于信息交换、遥感、监测和控制的多功能设备联网，几乎在每个领域都有很好的应用。该项目以人体循环系统为动力，引入创新方法，规范未来物联网网络的设计和分析，提供更可靠的人机界面，包括脑控应用。潜在地，它可以为智能家居、智能电网、健康监测、国家安全、电子商务以及许多可以从快速可靠的人-设备通信中受益的未来应用建立理想的人-设备平台做出重大贡献。此外，通过将该项目产生的技术进步整合到密歇根州立大学的课程开发和推广活动中，该项目有望在无线通信和物联网网络领域培养高技能和多样化的劳动力。本项目旨在建立统一的网络建模和表征框架，并开发物联网网络设计、管理和性能评估的创新技术，从而实现未来的人-设备用户界面。更具体地说，该项目计划：1)开发一个统一的框架，以表征以网络为中心的管理和特设灵活性的融合。新框架将所有现有网络作为特例，使网络性能的定量评估更易于处理和系统化；2)开发基于统一框架的创新网络设计和性能分析方法。优化拓扑设计将提供吞吐量最大化。将进行稳定性、延迟和效率分析，为网络性能评估提供基准。进行分集增强和动态路由协议设计，增强网络的安全性和可靠性；3)为大规模多输入多输出系统开发创新的基于机器学习的重叠用户分组技术。这些新技术可以大大提高系统容量，并确保对所有物联网设备的全覆盖；4)应用通信领域的先进工具，进行多层次的计算脑分析，实现更精确的脑信号提取。多层次的大脑分析将有助于更好地理解大脑功能、功能障碍和大脑处理能力。准确的脑信号提取为人类思维与脑控设备之间可靠接口的发展奠定了基础，而脑控设备将成为物联网家族的新成员。该项目的变革性研究包括新的物联网网络设计、管理和性能评估技术，以及人类思维和大脑控制设备之间的新用户界面。

The Internet of Things (IoT), which networks versatile devices for information exchange, remote sensing, monitoring and control, is finding promising applications in nearly every field. Motivated by the human circulation system, this project introduces innovative methodologies that can regulate the design and analysis of future IoT networks, and provide more reliable human-device interface, including brain-controlled applications. Potentially, it can make significant contributions to the establishment of an ideal human-device platform for Smart Home, Smart Grid, health monitoring, national security, e-commerce, as well as many future applications that can benefit from fast and reliable human-device communications. Moreover, by integrating the technological advances resulting from this project into the curriculum development and outreach activities at Michigan State University, significant impacts are expected from this project on training a highly-skilled and diverse workforce in the areas of wireless communications and IoT networking. This project aims to develop a unified framework for network modeling and characterization, and to develop innovative techniques for IoT network design, management and performance evaluation, so as to enable the future human-device user interface. More specifically, this project plans to: 1) develop a unified framework to characterize the convergence of network centric management and ad hoc flexibility. The new framework includes all the existing networks as special cases, and makes quantitative network performance evaluation more tractable and systematic; 2) develop innovative network design and performance analysis methodologies based on the unified framework. Optimal topology design will be provided for throughput maximization. Stability, delay and efficiency analysis will be conducted to provide benchmarks on network performance evaluation. Diversity enhancement and dynamic routing protocol design will be carried out to reinforce network security and reliability; 3) develop innovative machine learning based overlapping user grouping techniques for massive multi-input multi-output systems. These new techniques can greatly increase the system capacity and ensure full coverage for all the IoT devices; and 4) apply the advanced tools in communications to perform multi-level computational brain analysis, and to achieve more accurate brain signal extraction. Multi-level brain analysis will result in better understanding on brain functions, dysfunctions and brain processing capacity. Accurate brain signal extraction lays the foundation for the development of reliable interface between the human mind and brain-controlled devices, which will be new members in the IoT family. Transformative research in this project includes new IoT network design, management and performance evaluation techniques, as well as the new user interface between human mind and brain-controlled devices.