Collaborative Research: SWIFT: LARGE: Dynamics and Security Aware Predictive Spectrum Sharing with Active and Passive Users

协作研究：SWIFT：大型：与主动和被动用户进行动态和安全感知预测频谱共享

• 批准号: 2030249
• 负责人: Goce Trajcevski
• 金额: $ 45.83万
• 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: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2030249&HistoricalAwards=false
• 关键词: Collaborative; Research; SWIFT; LARGE; Dynamics

This project is helping to lay the foundation for more efficient and secure access to wireless spectrum while protecting essential existing uses of that spectrum. This access is critical to meet the demands from both legacy and emerging wireless applications, that have potential benefits across society. To this end, we are developing novel sensors, algorithmic solutions, models and architecture for future Data-Aware Spectrum Access Systems (DASASs) that will extend the current state-of-the-art to enable date driven solutions for spectrum access. Other impacts of this project include outreach to K-12 education, training and mentoring students including undergraduates and under-represented minorities, and developing new courses related to the proposed research.The envisioned system will leverage extensive low-cost sensing to support spectrum sharing across different frequency bands at different timescales for a rich set of different use cases including static and mobile users, active and passive spectrum utilization, and planned versus random uses. We are taking a multidisciplinary approach to design this system including: developing low cost spectrum sensors that utilize novel low-power distributed algorithms; understanding how to efficiently integrate and query the data obtained from these algorithms with other sources (e.g., weather, spatial density of demands) and use it to predictively trigger algorithms for spectrum allocation decisions; developing new and scalable spectrum allocation algorithms that use the acquired data to manage a wide range of users of the spectrum while accounting for market competition; and ensuring that the resulting system can leverage the available data to be secure against malicious attackers and unintentional user behavior.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.

该项目有助于为更有效和更安全地访问无线频谱奠定基础，同时保护该频谱的基本现有用途。 这种访问对于满足传统和新兴无线应用的需求至关重要，这些应用对整个社会都有潜在的好处。 为此，我们正在为未来的数据感知频谱接入系统（DASAS）开发新的传感器、算法解决方案、模型和架构，这些系统将扩展当前的最新技术，以实现频谱接入的数据驱动解决方案。 该项目的其他影响包括扩展到K-12教育，培训和指导包括本科生和代表性不足的少数民族在内的学生，以及开发与拟议研究相关的新课程。设想的系统将利用广泛的低成本感测来支持不同时间尺度的不同频段的频谱共享，用于丰富的不同用例，包括静态和移动的用户，主动和被动频谱利用，以及计划使用与随机使用。 我们正在采取多学科的方法来设计这个系统，包括：开发低成本的频谱传感器，利用新的低功耗分布式算法;了解如何有效地整合和查询从这些算法获得的数据与其他来源（例如，天气、需求的空间密度），并使用它来预测性地触发用于频谱分配决策的算法;开发新的和可扩展的频谱分配算法，其使用所获取的数据来管理频谱的广泛用户，同时考虑市场竞争;并确保生成的系统可以利用可用数据来抵御恶意攻击者和无意的用户行为。该奖项反映了NSF的法定使命，并已被认为是值得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的支持。

项目成果

Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems

• DOI: 10.1109/tsmc.2023.3252079
• 发表时间: 2023-07
• 期刊: IEEE Transactions on Systems, Man, and Cybernetics: Systems
• 作者: Fan Zhou;Tangjiang Qian;Yuhua Mo;Zhangtao Cheng;Chunjing Xiao;Jin Wu;Goce Trajcevski

HydroFlow: Towards probabilistic electricity demand prediction using variational autoregressive models and normalizing flows

• DOI: 10.1002/int.22864
• 发表时间: 2022-03
• 期刊: International Journal of Intelligent Systems
• 影响因子: 7
• 作者: Fan Zhou;Zhiyuan Wang;Ting Zhong;Goce Trajcevski;A. Khokhar

Geo-Awareness of Learnt Citations Prediction for Scientific Publications (Demo Paper)

科学出版物学习引文预测的地理意识（演示论文）

• DOI: 10.1145/3615896.3628341
• 发表时间: 2023
• 期刊: ACM SIGSPATIAL
• 作者: Li, Ce;Postler, Will;Johnson, Ian;Brinkmann, Paul;Gossling, Evan;Gorlewski, Bailey;Trajcevski, Goce
• 通讯作者: Trajcevski, Goce

Integrating Heterogeneous Sources for Learned Prediction of Vehicular Data Consumption

• DOI: 10.1109/mdm55031.2022.00029
• 发表时间: 2022-06
• 期刊: 2022 23rd IEEE International Conference on Mobile Data Management (MDM)
• 作者: Andi Zang;Xiaofeng Zhu;Ce Li;Fan Zhou;Goce Trajcevski

CSD-CMAD: Coupling Similarity and Diversity for Clustering Multivariate Astrophysics Data

• DOI: 10.1145/3474717.3483989
• 发表时间: 2021-11
• 期刊: Proceedings of the 29th International Conference on Advances in Geographic Information Systems
• 作者: Xu Teng;Thomas Beckler;Bradley Gannon;Benjamin Huinker;Gabriel Huinker;Koushhik Kumar;Christina Marquez;Jacob Spooner;Goce Trajcevski;Prabin Giri;A. Dotter;J. Andrews;S. Coughlin;Y. Qin;J. G. Serra-Perez;N. Tran;Jaime Roman-Garja;K. Kovlakas;E. Zapartas;S. Bavera;D. Misra;T. Fragos