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

如今，蜂窝网络和电网运营商测量了大量数据，这些数据可以为城市范围内的移动性和拥堵模式提供丰富的见解。与出租车车队运营商等独立的外部实体共享此类实时社会趋势，可以增强城市规模的资源分配和控制任务，如电动出租车路线和电池存储优化。然而，丰富时间序列的所有者和外部控制机构必须跨越数据边界进行通信，这限制了他们可以共享的数据量和范围。该项目将开发新的算法和系统，以联合压缩、匿名和定价丰富的时间序列数据，从而只共享跨组织边界的最低限度的任务相关数据。通过强调通信效率，所开发的算法将激励未来智能城市的数据共享和协作。这项工作的关键动机是，今天的时间序列数据的表示独立于最终控制任务设计，这往往会导致发送不必要的时间特征，揭示私有特征，以及最显著的趋势被低估。因此，该项目将开发一种统一的方法来共同设计丰富的时间序列数据的简洁、私有表示以及最终的控制任务。在这里，联合设计意味着预测表示是在控制目标的更广泛的上下文中学习的，同时考虑到带宽限制、隐私以及数据处理的经济成本和激励。这些算法将计算控制器对预测误差的敏感度，预测误差可能由数据压缩、预测不确定性以及现代隐私工具注入的人工噪声引起。至关重要的是，控制器的灵敏度将转而传递给网络运营商，以指导其优化和学习(例如，共同设计)简洁的、与任务相关的预测表示，该表示掩盖了私有属性，并根据它们对控制的重要性自然地对时间特征进行定价。例如，这项研究将使运营商能够灵活地使用相同的基本小区需求数据，以强调出租车路线在高峰时间的可变性，同时无缝地向移动视频流媒体公司提供细粒度的吞吐量预测，而不会揭示用户的私人移动性。最后，该项目中的案例研究将被整合到德克萨斯大学奥斯汀分校和康奈尔大学基于学习的控制课程中。更广泛的影响还包括外展和包容努力，以吸引来自STEM领域历史上代表性不足的群体的学生。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.