Collaborative Research: CISE-MSI: RCBP-RF: CNS: Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

合作研究：CISE-MSI：RCBP-RF：CNS：无基站传感器网络中真实且最优的数据保存：集成博弈论和网络流方法

• 批准号: 2131309
• 负责人: Yutian Chen
• 金额: $ 28.95万
• 依托单位: California State University-Long Beach Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2131309&HistoricalAwards=false
• 关键词: Collaborative; Research; CISE; MSI; RCBP

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).The goal of the project is to create a truthful and optimal resource allocation framework for emerging base station-less sensor networks. As such networks are deployed in challenging environments without data-collecting base station (e.g., underwater exploration), the paramount task is to preserve large amounts of generated data inside the networks before uploading opportunities become available. In a distributed setting and under different control, however, the sensor nodes with limited resources (i.e., energy power and storage spaces) could behave selfishly in order to save their own resources and maximize their own benefits. The tension between node-centric selfishness and data-centric data preservation gives rise to new challenge that calls for integrated study of game theory (the science of strategic interaction), and network flows (that studies how to move network objects efficiently and effectively). This project deploys following research thrusts. First, selfish data preservation is analyzed in terms of Nash equilibrium, price of anarchy, price of stability, and Shapley scheme. Second, mechanism design approach is used to identify the limitations of existing methodology and propose new incentive mechanisms. Third, a suite of new data preservation and data aggregation games are designed to incorporate network-specific features such as data values and data spatial correlations. All the research thrusts intertwine game-theoretic and network flow technique to achieve the truthful and optimal data preservation. Finally, the designed techniques will be evaluated by simulations, existing network flow and game theory software, as well as CloudBank.By preserving large amounts of data of the physical world otherwise inaccessible, base station-less sensor networks provide a comprehensive view of scientific frontiers including scientific exploration, disaster warning and climate change, thus benefiting the society. This project is collaborated between California State University Long Beach Economics Department and California State University Dominguez Hills Computer Science Department. This cross-institutional and interdisciplinary collaboration provides an integrative research and education experience for students. The educational goal is not just recruiting and working with a few best students but inspiring and educating as many underrepresented students as possible at both institutions. Planned activities include student campus visit and poster exhibition, intra-campus collaboration, conference presentation and participation, curriculum update and development, and integrating with existing minority-serving programs at both institutions.Details of the project can be found at https://web.csulb.edu/~ychen7/bsn_gametheory/. This website will be updated regularly as the research progresses and will be maintained for public view for five to ten years.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.

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。该项目的目标是为新兴的无基站传感器网络创建一个真实和最佳的资源分配框架。由于这样的网络被部署在没有数据收集基站（例如，水下勘探），首要任务是在网络内保存大量生成的数据，然后才有机会上传。然而，在分布式设置中并且在不同的控制下，具有有限资源的传感器节点（即，能源、电力和存储空间）可能会自私地行事，以节省自己的资源并使自己的利益最大化。以节点为中心的自私和以数据为中心的数据保存之间的紧张关系带来了新的挑战，需要对博弈论（战略交互科学）和网络流（研究如何有效地移动网络对象）进行综合研究。该项目部署了以下研究重点。首先，自私的数据保存分析的纳什均衡，价格的无政府状态，价格的稳定性和Shapley计划。第二，运用机制设计的方法，找出现有方法的局限性，提出新的激励机制。第三，设计了一套新的数据保存和数据聚合游戏，以结合网络特定的功能，如数据值和数据空间相关性。所有的研究都是通过运用中断博弈理论和网络流技术来实现真实、最优的数据保存。最后，通过模拟、现有的网络流和博弈论软件以及CloudBank对设计的技术进行评估。无基站传感器网络通过保存物理世界的大量数据，为科学探索、灾害预警和气候变化等科学前沿提供了全面的视角，从而造福社会。该项目是由加州州立大学长滩经济系和加州州立大学多明格斯山计算机科学系合作完成的。这种跨机构和跨学科的合作为学生提供了一个综合的研究和教育经验。教育目标不仅仅是招募和与少数最好的学生一起工作，而是在这两个机构中激励和教育尽可能多的代表性不足的学生。计划的活动包括学生校园参观和海报展览，校内合作，会议演示和参与，课程更新和开发，以及与两所机构现有的少数民族服务计划相结合。该项目的详细信息可以在https://web.csulb.edu/~ychen7/bsn_gametheory/上找到。该网站将随着研究的进展定期更新，并将保持五到十年的公众视野。该奖项反映了NSF的法定使命，并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，被认为值得支持。

项目成果

MIF: Optimizing Information Freshness in Intermittently Connected Sensor Networks

• DOI: 10.1145/3491315.3491338
• 发表时间: 2021-11
• 期刊: Proceedings of the 15th International Conference on Underwater Networks & Systems
• 作者: Howard Luu;Hung L. Ngo;Bin Tang;M. Beheshti

Truthful and Optimal Data Preservation in Base Station-less Sensor Networks: An Integrated Game Theory and Network Flow Approach

• DOI: 10.1145/3606263
• 发表时间: 2024-01-01
• 期刊: ACM TRANSACTIONS ON SENSOR NETWORKS
• 影响因子: 4.1
• 作者: Yu，Yuning;Hsu，Shanglin;Tang，Bin
• 通讯作者: Tang，Bin

Service Function Chain Placement in Cloud Data Center Networks: A Cooperative Multi-agent Reinforcement Learning Approach

• DOI: 10.1007/978-3-031-23141-4_22
• 发表时间: 2022
• 期刊: 2022 International Symposium on Electronics and Telecommunications (ISETC)
• 作者: Lynn Gao;Yutian Chen;Bin Tang

Data-VCG: A Data Preservation Game for Base Station-less Sensor Networks with Performance Guarantee

Data-VCG：具有性能保证的无基站传感器网络的数据保存游戏

• DOI: 10.23919/ifipnetworking57963.2023.10186405
• 发表时间: 2023
• 期刊: IFIP Networking 2023.
• 作者: Ly, Jennifer;Chen, Yutian;Tang, Bin
• 通讯作者: Tang, Bin

On the Performance of Nash Equilibria for Data Preservation in Base Station-less Sensor Networks

• DOI: 10.1109/mass58611.2023.00038
• 发表时间: 2023-09
• 期刊: 2023 IEEE 20th International Conference on Mobile Ad Hoc and Smart Systems (MASS)
• 作者: Giovanni Rivera;Yutian Chen;Bin Tang