CC* Integration: End-to-End Software-Defined Cyberinfrastruture for Smart Agriculture and Transportation

CC* 集成：用于智能农业和交通的端到端软件定义网络基础设施

• 批准号: 1827211
• 负责人: Hongwei Zhang
• 金额: $ 99.99万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-10-01 至 2022-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1827211&HistoricalAwards=false
• 关键词: CC; Integration; End; Software; Defined

Imaging and other sensor-based understanding of plant behavior is becoming keyto new discoveries in plant genotypes leading to more productive and environment-friendly farming.Similarly, distributed sensing is seen as a key component of a safe, efficient, and sustainable autonomous transportation systems.Existing research and education in agriculture and transportation systems are constrained by the lack of connectivity between field-deployed testbed equipment and computing infrastructure. To realize that connectivity, this project proposes to deploy CyNet wireless networks toconnect experimental science testbeds to high-performance cloud computing infrastructures.The CyNet project will:1) deploy Predictable, Reliable, Real-time, and high-Throughput (PRRT) wireless networking solutions using the standards-compliant, open-source Open Air Interface software framework and commodity Universal Software Radio Peripheral (USRP) hardware;2) integrate these wireless networks with software defined networks to seamlessly integrate outdoor cameras, sensors, and autonomous vehicles, and connect these components to high performance cloud computing systems;3) implement an infrastructure virtualization system that partitions CyNet into programmable, isolated experiments; and4) create an infrastructure management system that performs admission and access control and establishes specified resource allocation policies.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.

对植物行为的成像和其他基于传感器的理解正在成为植物基因型新发现的关键，从而导致更高产和环境友好的农业。同样，分布式传感被视为安全，高效，和可持续的自主运输系统。农业和运输系统方面的现有研究和教育受到缺乏实地-已部署的试验台设备和计算基础设施。为了实现这种连接，该项目提出部署CyNet无线网络，将实验科学测试平台连接到高性能云计算基础设施。CyNet项目将：1）使用符合标准的开源开放式空中接口软件框架和通用软件无线电外设（USRP）硬件部署可预测、可靠、实时和高吞吐量（PRRT）的无线网络解决方案; 2）将这些无线网络与软件定义网络进行整合，以无缝集成户外摄像头、传感器和自动驾驶汽车，并将这些组件连接到高性能云计算系统; 3）实现一个基础设施虚拟化系统，将CyNet划分为可编程的、隔离的实验; 4）创建一个基础设施管理系统，执行准入和访问控制，并建立指定的资源分配策略。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Wireless-Powered Machine-to-Machine Multicasting in Cellular Networks

• DOI: 10.1109/tgcn.2020.2986216
• 发表时间: 2020-04
• 期刊: IEEE Transactions on Green Communications and Networking
• 影响因子: 4.8
• 作者: A. Almasoud;A. Kamal

Look-Ahead and Learning Approaches for Energy Harvesting Communications Systems

• DOI: 10.1109/tgcn.2019.2953644
• 发表时间: 2020-03-01
• 期刊: IEEE TRANSACTIONS ON GREEN COMMUNICATIONS AND NETWORKING
• 影响因子: 4.8
• 作者: Masadeh, Ala'eddin;Wang, Zhengdao;Kamal, Ahmed E.
• 通讯作者: Kamal, Ahmed E.

Analysis of Joint Scheduling and Power Control for Predictable URLLC in Industrial Wireless Networks

• DOI: 10.1109/icii.2019.00041
• 发表时间: 2019-11
• 期刊: 2019 IEEE International Conference on Industrial Internet (ICII)
• 作者: Ling Wang;Hongwei Zhang

Selector-Actor-Critic and Tuner-Actor-Critic Algorithms for Reinforcement Learning

用于强化学习的 Selector-Actor-Critic 和 Tuner-Actor-Critic 算法

• DOI: 10.1109/wcsp.2019.8928124
• 发表时间: 2019
• 期刊: International Conference on Wireless Communications and Signal Processing
• 作者: Masadeh, Ala'eddin;Wang, Zhengdao;Kamal, Ahmed E.
• 通讯作者: Kamal, Ahmed E.

Cyber-Physical Scheduling for Predictable Reliability of Inter-Vehicle Communications

• DOI: 10.1109/tvt.2020.2968591
• 发表时间: 2020-01
• 期刊: IEEE Transactions on Vehicular Technology
• 影响因子: 6.8
• 作者: Chuan Li;Hongwei Zhang;Tianyi Zhang;J. Rao;L. Wang;G. Yin