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

EAGER：物联网网络的仿生无线系统设计：从传感器到大脑控制应用

• 批准号: 1744604
• 负责人: Tongtong Li
• 金额: $ 15万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2019-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744604&HistoricalAwards=false
• 关键词: EAGER; Biomimetic; wireless; system; design

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.

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前景。受人体循环系统的启发，该项目引入了创新的方法，可以规范未来物联网网络的设计和分析，并提供更可靠的人机接口，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融入密歇根州立大学的课程开发和推广活动，预计该项目将对培训无线通信和物联网网络领域的高技能和多样化的劳动力产生重大影响。该项目旨在为网络建模和表征开发一个统一的框架，并为物联网网络设计、管理和性能评估开发创新技术，以实现未来的人机用户界面。更具体地说，该项目计划：1）开发一个统一的框架，以表征网络中心管理和ad hoc灵活性的融合。新框架将现有网络作为特例，使定量网络性能评估更易于处理和系统化; 2）在统一框架的基础上发展创新的网络设计和性能分析方法。最佳的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，以提供网络性能评估的基准。 将进行多样性增强和动态路由协议设计，以加强网络的安全性和可靠性; 3）开发基于机器学习的创新重叠用户分组技术，用于大规模多输入多输出系统。这些新技术可以大大增加系统容量，并确保所有物联网设备的全覆盖; 4）应用先进的通信工具进行多层次的计算大脑分析，并实现更准确的大脑信号提取。多层次的脑分析将导致更好地了解大脑功能，功能障碍和大脑处理能力。准确的大脑信号提取为人类思维和脑控设备之间的可靠接口的开发奠定了基础，这些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，以及人类思维和脑控设备之间的新用户界面。

项目成果

Optimal Construction of Regenerating Code Through Rate-Matching in Hostile Networks

• DOI: 10.1109/tit.2017.2694441
• 发表时间: 2015-11
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Jian Li;Tongtong Li;Jian Ren

End-to-End Throughput Analysis of Multi-Hop Wireless Networks Using Stochastic Geometry

使用随机几何的多跳无线网络的端到端吞吐量分析

• DOI: 10.4108/eai.13-7-2017.2271022
• 发表时间: 2017
• 期刊: MOBIMEDIA'17 Proceedings of the 10th EAI International Conference on Mobile Multimedia Communications
• 作者: Liang, Yuan;Ren, Jian;Li, Tongtong
• 通讯作者: Li, Tongtong