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，几乎在每个领域都找到了有前景的应用。在人体循环系统的推动下，该项目引入了创新方法，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界面，包括大脑控制的应用程序。它有可能为智能家居、智能电网、健康监测、国家安全、电子商务以及许多可以受益于快速可靠的人体设备的未来应用建立理想的人体设备平台做出重大贡献通讯。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，预计该项目将对无线通信和物联网领域培训高技能和多样化的劳动力产生重大影响。该项目旨在开发一个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，以实现未来的人机用户界面。更具体地说，该项目计划： 1) 开发一个统一框架来表征以网络为中心的管理和临时灵活性的融合。新框架将所有现有网络作为特例，使定量网络性能评估更加容易处理和系统化； 2）基于统一框架开发创新的网络设计和性能分析方法。将提供最佳拓扑设计以实现吞吐量最大化。将进行稳定性、延迟和效率分析，为网络性能评估提供基准。 进行多样性增强和动态路由协议设计，增强网络安全性和可靠性； 3）为大规模多输入多输出系统开发基于创新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，保证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大脑计算分析，实现更准确的大脑信号提取。多层次的大脑分析将有助于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开发人类思维和大脑控制设备之间的可靠接口奠定了基础，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，以及人类思维和大脑控制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

敌对网络中通过速率匹配重新生成代码的优化构造

• DOI: 10.1109/tit.2017.2694441
• 发表时间: 2015-11-07
• 期刊: IEEE Transactions on Information Theory
• 影响因子: 2.5
• 作者: Jian Li;Tongtong Li;Jian Ren
• 通讯作者: Jian Ren

End-to-End Throughput in Multi-Hop Wireless Networks With Random Relay Deployment

具有随机中继部署的多跳无线网络中的端到端吞吐量

• DOI: 10.1109/tsipn.2018.2806846
• 发表时间: 2018-01
• 期刊: IEEE Transactions on Signal and Information Processing over Networks
• 影响因子: 3.2
• 作者: Liang, Yuan;Li, Tongtong
• 通讯作者: Li, Tongtong

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: 2017-07
• 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

Overlapping User Grouping in IoT Oriented Massive MIMO Systems

面向物联网的大规模 MIMO 系统中的重叠用户分组

• DOI: 10.1109/access.2017.2729878
• 发表时间: 2017-01
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: Tian, Run;Liang, Yuan;Tan, Xuezhi;LI, Tongtong
• 通讯作者: LI, Tongtong