Program

Day:  Monday, 30 October
Time:  2:10pm - 5:00pm
Location:
WS-08 Quality, Age, and Value of Information for Tactical Networks

 

 

 

Keynote: Timely Communications for Remote Inference and Estimation: A First Principles Approach

Abstract: The evolution of Artificial Intelligence, Control, and Communications technologies has given rise to a new era of networked intelligent systems, which include autonomous driving, remote surgery, real-time surveillance, video analytics, and factory automation. Timely Inference is vital in these systems, where a trained neural network infers time-varying targets (e.g., the locations of vehicles and pedestrians) based on observations (e.g., video frames) captured by a sensing node (e.g., camera). Due to communication delay, the data delivered to the neural network may not be fresh, impacting both inference accuracy and overall system performance. In this talk, we will first examine the influence of information freshness on remote inference and estimation. One might assume that inference and estimation errors degrade monotonically as the data becomes stale. However, by a local information geometric analysis, we reveal that this assumption is true when the time-sequence data used for remote inference and estimation can be closely approximated as a Markov chain; but it is not true when the data sequence is far from Markovian. Hence, inference and estimation errors are functions of the Age of Information (AoI), whereas the function is not necessarily monotonic. This analysis provides an information-theoretic interpretation of information freshness. The second part of the talk focuses on the design of communication systems optimized for remote inference and estimation. We introduce a novel "selection-from-buffer" model for data transmission, which is more general than the "generate-at-will" model used in earlier AoI studies. Low-complexity scheduling strategies are developed to minimize inference and estimation errors. Trace-driven evaluations demonstrate the potential of these communication strategies to reduce inference and estimation errors by up to 10-10000 times. We will also discuss future directions, such as context-aware status updating and situational awareness maximization in safety-critical systems. 

Bio: Yin Sun is the Godbold Associate Professor in the Department of Electrical and Computer Engineering at Auburn University, Alabama. He received his B.Eng. and Ph.D. degrees in Electronic Engineering from Tsinghua University, in 2006 and 2011, respectively. He was an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University from 2017-2023 and a Postdoctoral Scholar and Research Associate at the Ohio State University from 2011-2017. His research interests include Wireless Networks, Machine Learning, Semantic Communications, Age of Information, Information Theory, and Robotic Control. He is also interested in applying AI and Machine Learning techniques in Agricultural, Food, and Nutrition Sciences. He founded the Age of Information (AoI) Workshop in 2018 and the Modeling and Optimization in Semantic Communications (MOSC) Workshop in 2023. His articles received the Best Student Paper Award of the IEEE/IFIP WiOpt 2013, Best Paper Award of the IEEE/IFIP WiOpt 2019, runner-up for the Best Paper Award of ACM MobiHoc 2020, and 2021 Journal of Communications and Networks (JCN) Best Paper Award. He co-authored a monograph Age of Information: A New Metric for Information Freshness. He received the Auburn Author Award of 2020, the National Science Foundation (NSF) CAREER Award in 2023, and was named a Ginn Faculty Achievement Fellow in 2023. He is a Senior Member of the IEEE and a Member of the ACM. His research is sponsored by the National Science Foundation, the Army Research Office, the Office of Naval Research, and the United States Department of Agriculture.

 

[Program]