Collaborative Research: CPS: Small: Co-Design of Prediction and Control across Data Boundaries: Efficiency, Privacy, and Markets

协作研究：CPS：小型：跨数据边界的预测和控制的协同设计：效率、隐私和市场

• 批准号: 2133403
• 负责人: Ao Tang
• 金额: $ 25万
• 依托单位: Cornell 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: 2021-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2133403&HistoricalAwards=false
• 关键词: Collaborative; Research; CPS; Small; Co

Today, operators of cellular networks and electricity grids measure large volumes of data, which can provide rich insights into city-wide mobility and congestion patterns. Sharing such real-time societal trends with independent, external entities, such as a taxi fleet operator, can enhance city-scale resource allocation and control tasks, such as electric taxi routing and battery storage optimization. However, the owner of a rich time series and an external control authority must communicate across a data boundary, which limits the scope and volume of data they can share. This project will develop novel algorithms and systems to jointly compress, anonymize, and price rich time series data in a way that only shares minimal, task-relevant data across organizational boundaries. By emphasizing communication efficiency, the developed algorithms will incentivize data sharing and collaboration in future smart cities.The key motivation of this work is that today's representations of time series data are designed independently of an ultimate control task, which often causes unnecessary temporal features to be sent, private features to be revealed, and the most salient trends to be under-valued. Accordingly, this project will develop a unified approach to co-design succinct, private representations of rich time series data along with an ultimate control task. Here, co-design means that the forecast representation is learned within the broader context of a control objective while accounting for bandwidth constraints, privacy, and economic costs and incentives for data processing. The algorithms will compute a controller's sensitivity to prediction errors, which can arise from data compression, forecast uncertainty, as well as artificial noise injected by modern privacy tools. Crucially, the controller's sensitivity will in turn be relayed to a network operator to guide its optimization and learning (e.g., co-design) of a concise, task-relevant forecast representation that masks private attributes and naturally prices temporal features by their importance to control. The research will, for example, enable operators to flexibly use the same underlying cell demand data to emphasize peak-hour variability for taxi routing, while seamlessly delivering fine-grained throughput forecasts to a mobile video streaming company without revealing private user mobility. Finally, the case studies in this project will be integrated into courses on learning-based control at UT Austin and Cornell. Broader impacts also include outreach and inclusion efforts to engage students from groups that have historically been under-represented in STEM fields.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.

如今，蜂窝网络和电网的运营商测量了大量数据，这可以为全市范围的流动性和拥塞模式提供丰富的见解。与独立的外部实体（例如出租车车队运营商）共享这种实时社会趋势，可以增强城市规模的资源分配和控制任务，例如电动出租车路由和电池存储优化。但是，富裕时间序列和外部控制权限的所有者必须在数据边界上进行通信，这限制了它们可以共享的数据范围和量。该项目将开发新颖的算法和系统，以共同压缩，匿名和价格丰富的时间序列数据，以一种在组织边界之间共享最小，与任务相关的数据的方式。通过强调沟通效率，开发的算法将激励数据共享和协作在未来的智能城市中。这项工作的关键动机是，当今时间序列数据的表示形式独立于设计而不是最终的控制任务，这通常会导致要发送的不必要的时间功能，私人功能，以及最不可能的趋势，以及偏离偏好的最重要趋势。因此，该项目将开发一种统一的方法来共同设计丰富时间序列数据的简洁，私人表示以及最终的控制任务。在这里，共同设计意味着在控制目标的更广泛背景下学习了预测表示，同时考虑了带宽约束，隐私，经济成本以及数据处理的激励措施。该算法将计算控制器对预测错误的敏感性，这可能是由数据压缩，预测不确定性以及现代隐私工具注入的人造噪声引起的。至关重要的是，控制器的敏感性将依次转移到网络运营商中，以指导其优化和学习（例如，共同设计）简明，与任务相关的预测表示，该表示掩盖了私人属性，并通过其重要性来控制私人属性，并且自然而然地给予时间特征。例如，这项研究将使操作员能够灵活地使用相同的基础电池需求数据来强调出租车路由的高峰时段可变性，同时无缝向移动视频流媒体公司提供细粒度的吞吐量预测而不揭示私人用户移动性。最后，该项目中的案例研究将纳入UT Austin和Cornell的基于学习控制的课程。更广泛的影响还包括宣传和包容性的工作，以吸引从历史上不足以代表性的团体的学生吸引学生。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。

项目成果

Optimal Compression for Minimizing Classification Error Probability: An Information-Theoretic Approach

最小化分类错误概率的最佳压缩：一种信息论方法

• 发表时间: 2023
• 期刊: ICASSP 2024
• 作者: Gao, Jingchao;Tang, Ao;Xu, Weiyu
• 通讯作者: Xu, Weiyu

Data Sharing and Compression for Cooperative Networked Control

• 发表时间: 2021-09
• 作者: Jiangnan Cheng;M. Pavone;S. Katti;Sandeep P. Chinchali;A. Tang

Task-aware Network Coding over Butterfly Network

蝴蝶网络上的任务感知网络编码

• 发表时间: 2023
• 期刊: 2023 IEEE International Symposium on Information Theory (ISIT
• 作者: Cheng, Jiangnan;Tang, Ao
• 通讯作者: Tang, Ao

Task-aware Privacy Preservation for Multi-dimensional Data

• 发表时间: 2021-10
• 作者: Jiangnan Cheng;A. Tang;Sandeep Chinchali

Linear-Quadratic-Gaussian Control with Time-Varying Disturbance Forecast

具有时变干扰预测的线性二次高斯控制

• 发表时间: 2022
• 期刊: IEEE Conference on Decision and Control (CDC
• 作者: Cheng, Jiangnan;Tang, Ao
• 通讯作者: Tang, Ao