CAREER: Directed Information Theory for Networked Control Systems in Big Data Regime

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Modern control systems supporting today’s critical infrastructure (e.g., transportation network, power systems) are comprised of a large number of computational devices interconnected over communication networks. Such systems are commonly called Networked Control Systems (NCSs), and have been the subject of research interest over the last two decades, both from theoretical and practical perspectives. The maturation of technologies in both wireless and wired communication made various NCS concepts realizable (e.g., connected cars, remote surgery, cloud-based control), and the roles of NCSs will further grow in the future. Recently, however, the advancements of Artificial Intelligence (AI) technologies are increasing demand for AI-powered control algorithm implementations over NCS architectures. While these new trends exemplify the remarkable capabilities of NCSs, they raise unprecedented challenges to the current form of NCS technologies in terms of: (1) capacity to incorporate Big Data contents that support emerging AI-based control schemes, (2) scalability to accommodate a large number of players within limited network resources, and (3) security against and privacy from potential adversaries. The purpose of this CAREER proposal is to apply a novel information flow optimization technique, termed Optimal Information Flow (OIF) synthesis, to address a few selected, yet representative, technological challenges in each of the three areas identified above. The key enabler, as well as the uniqueness, of the proposed approach is the novel use of directed information, an information-theoretic quantity that allows for the identification and prioritization of task-relevant information flow over the network. Our starting point is the zero-delay rate-distortion theory and its successful applications to single-channel NCSs. To address (1), we apply directed information to develop a real-time, task-dependent data compression algorithm for high-volume data streams. Our target applications include cloud-based visual servoing for autonomous driving, where we study the extent that data-rate reduction from video streams is possible without deteriorating control performance. To tackle (2), we pursue new approaches to multi-channel NCS design by blending the existing NCS theory with network information theory. We will demonstrate that an appropriate control-communication co-design effectively reduces fuel consumption of autonomous vehicle platoons. For (3), we will study both cryptographic and non-cryptographic privacy preserving mechanisms for cloud-based control systems. We propose design principles for achieving the optimal utility-privacy balance based on the OIF synthesis. Instead of carrying out these three research tasks in a domain-specific manner, we emphasize the underlying roles of directed information in each task, thereby contributing to the well-recognized academic challenge of integrating control theory with information theory. This CAREER proposal also involves innovative educational activities, including the development of a unique graduate-level curriculum on NCSs, in order to maximize the synergistic effects between research and education.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.