CAREER: Semantic and Goal-oriented Status Updating for Real-time Inference, Monitoring, and Decision-Making

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Information usually has the greatest value when it is fresh. For instance, timely inference about the trajectories of nearby vehicles and pedestrians is imperative in autonomous driving, and real-time knowledge about the movements of surgical robots is essential for remote surgery. In these examples, the freshness of information is tightly related to its significance for the application. "Freshness" is an example of the "semantic" properties of the information. "Semantics" are properties that relate to the "goal" and "purpose" of information, which is to improve the performance of systems using the information. In semantic and goal-oriented status updating, the design objective is no longer sending as much as possible (i.e., increase throughput), or as fast as possible (i.e., reduce delay), without regard to the intended use of information. Instead, the transmitter should choose the right piece of information that is important for the system operating at the receiving end, and try to deliver the information to the receiver when it is most useful. This project provides innovative and foundational approaches to interpreting the freshness and semantics of information in a way that is both rigorous in theory and meaningful in practice, by characterizing the significance of such information in real-time inference, monitoring, and decision-making. Application-centric communications and scheduling techniques are developed to optimize the performance of these real-time systems. The research outcomes are expected to significantly impact numerous applications, ranging from artificial intelligence (AI) and robots to health, safety, and security, all of which are of paramount importance for the Nation's future. This project will also provide AI education and research experiences to K-12 students, undergraduate students, and students from underrepresented groups. This project develops novel semantic and goal-oriented status-updating techniques for a broad range of real-time inference, monitoring, and decision-making systems. The main goals of this project are (i) to use information-theoretic analyses and experimental studies to interpret how the freshness and semantics of information affect the performance of these real-time systems, (ii) to design new communication techniques and networking protocols for optimizing freshness/semantic measures and the system performance, and (iii) to explore the benefits of signal-aware status updating that goes beyond Age of Information to realize additional performance gain. The new status-updating techniques will be implemented to evaluate their practical performance. These research efforts are likely to lead to far-reaching impacts in the relationship of networking, machine learning, communications, and information theory at a fundamental level. 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.